Oskar Elek;Joseph N. Burchett;J. Xavier Prochaska;Angus G. Forbes
We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones’s (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones’s work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM’s self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.
{"title":"Monte Carlo Physarum Machine: Characteristics of Pattern Formation in Continuous Stochastic Transport Networks","authors":"Oskar Elek;Joseph N. Burchett;J. Xavier Prochaska;Angus G. Forbes","doi":"10.1162/artl_a_00351","DOIUrl":"10.1162/artl_a_00351","url":null,"abstract":"We present Monte Carlo Physarum Machine (MCPM): a computational model suitable for reconstructing continuous transport networks from sparse 2D and 3D data. MCPM is a probabilistic generalization of Jones’s (2010) agent-based model for simulating the growth of Physarum polycephalum (slime mold). We compare MCPM to Jones’s work on theoretical grounds, and describe a task-specific variant designed for reconstructing the large-scale distribution of gas and dark matter in the Universe known as the cosmic web. To analyze the new model, we first explore MCPM’s self-patterning behavior, showing a wide range of continuous network-like morphologies—called polyphorms—that the model produces from geometrically intuitive parameters. Applying MCPM to both simulated and observational cosmological data sets, we then evaluate its ability to produce consistent 3D density maps of the cosmic web. Finally, we examine other possible tasks where MCPM could be useful, along with several examples of fitting to domain-specific data as proofs of concept.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"22-57"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6720217/9930987/09931049.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39723623","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Review of Art in the Age of Machine Learning by Sofian Audry","authors":"Simon Penny","doi":"10.1162/artl_r_00352","DOIUrl":"10.1162/artl_r_00352","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"167-169"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44446348","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed. On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic. Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life. A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral h
{"title":"Editorial Introduction for 28:1","authors":"Alan Dorin;Susan Stepney","doi":"10.1162/artl_e_00378","DOIUrl":"10.1162/artl_e_00378","url":null,"abstract":"In this issue we are pleased to share with you a diverse set of reading materials. Sadly, we mark with an obituary the passing of Julian Miller, a researcher whose loss has been keenly felt within the community of Artificial Life researchers. He shall be sorely missed. On a much brighter note, the second installment of Chris Adami’s column exploring how artificial evolution might facilitate the design of General Intelligence is to be found within the pages of this issue. Adami explains how the indirect encoding of artificial brains to facilitate neuro-evolution might be managed. He discusses approaches to choosing an appropriate neuron, how to connect neurons to create a functioning network, how to train the network, and how the different options scale up to high levels of complexity. Drawing such connections between the techniques of Artificial Life and the concerns of Artificial Intelligence is key (we feel) to enhancing the recognition that embodiment, developmental processes, and evolutionary processes all have a role to play in the emergence of natural intelligence – to overlook this whilst striving for artificial general intelligence is likely problematic. Simon Penny, an artist long engaged in Artificial Life art and robotics, provides for us a critical review of a new book by Sofian Audry, Art in the Age of Machine Learning (MIT Press 2021). The title might seem to be slightly out of line with Artificial Life’s main focus, perhaps even more suited to an AI readership, but, as Penny points out, this isn’t necessarily the case. In fact, by presenting both the practical artistic-technological concerns of the day, and the philosophical issues these raise with respect to agency, creativity and art-making by machines, Audry is in fact delving into areas that should concern us as researchers of Artificial Life. A topic infrequently explored within the pages of this journal is the impact that Artificial Life has on human relationships. In Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots, Paolo Euron enters this space by examining “physical beauty according to the artistic, cultural, and philosophical traditions”, of sexbots. Since Euron focuses on the visual appearance of these humanoid robots, with this article we have adopted a new approach for the Artificial Life journal to widen the perspective. The text is therefore supported by commentaries the editors have sought from alternative points of view. Thomas Arnold provides comment on Euron’s work from the perspective of Human-Robot Interaction by assessing the ethics of sex robots and how concepts of human trust, dignity, and autonomy potentially influence our interactions with such machines. Maria O’Sullivan examines how human interactions with sexbots relate to gender power relations and our expectations and human norms of intimacy and vulnerability. She also considers the very real dangers now widely associated with the commodification of beauty and the potential for moral h","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"1-2"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42697751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The modern economy is both a complex self-organizing system and an innovative, evolving one. Contemporary theory, however, treats it essentially as a static equilibrium system. Here we propose a formal framework to capture its complex, evolving nature. We develop an agent-based model of an economic system in which firms interact with each other and with consumers through market transactions. Production functions are represented by a pair of von Neumann technology matrices, and firms implement production plans taking into account current price levels for their inputs and output. Prices are determined by the relation between aggregate demand and supply. In the absence of exogenous perturbations the system fluctuates around its equilibrium state. New firms are introduced when profits are above normal, and are ultimately eliminated when losses persist. The varying number of firms represents a recurrent perturbation. The system thus exhibits dynamics at two levels: the dynamics of prices and output, and the dynamics of system size. The model aims to be realistic in its fundamental structure, but is kept simple in order to be computationally efficient. The ultimate aim is to use it as a platform for modeling the structural evolution of an economic system. Currently the model includes one form of structural evolution, the ability to generate new technologies and new products.
{"title":"From Dynamics to Novelty: An Agent-Based Model of the Economic System","authors":"Gustavo Recio;Wolfgang Banzhaf;Roger White","doi":"10.1162/artl_a_00365","DOIUrl":"10.1162/artl_a_00365","url":null,"abstract":"The modern economy is both a complex self-organizing system and an innovative, evolving one. Contemporary theory, however, treats it essentially as a static equilibrium system. Here we propose a formal framework to capture its complex, evolving nature. We develop an agent-based model of an economic system in which firms interact with each other and with consumers through market transactions. Production functions are represented by a pair of von Neumann technology matrices, and firms implement production plans taking into account current price levels for their inputs and output. Prices are determined by the relation between aggregate demand and supply. In the absence of exogenous perturbations the system fluctuates around its equilibrium state. New firms are introduced when profits are above normal, and are ultimately eliminated when losses persist. The varying number of firms represents a recurrent perturbation. The system thus exhibits dynamics at two levels: the dynamics of prices and output, and the dynamics of system size. The model aims to be realistic in its fundamental structure, but is kept simple in order to be computationally efficient. The ultimate aim is to use it as a platform for modeling the structural evolution of an economic system. Currently the model includes one form of structural evolution, the ability to generate new technologies and new products.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"58-95"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43667718","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy—evolution overarching development—is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes—however natural—a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots’ morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.
{"title":"Morphological Development at the Evolutionary Timescale: Robotic Developmental Evolution","authors":"Fabien C. Y. Benureau;Jun Tani","doi":"10.1162/artl_a_00357","DOIUrl":"10.1162/artl_a_00357","url":null,"abstract":"Evolution and development operate at different timescales; generations for the one, a lifetime for the other. These two processes, the basis of much of life on earth, interact in many non-trivial ways, but their temporal hierarchy—evolution overarching development—is observed for most multicellular life forms. When designing robots, however, this tenet lifts: It becomes—however natural—a design choice. We propose to inverse this temporal hierarchy and design a developmental process happening at the phylogenetic timescale. Over a classic evolutionary search aimed at finding good gaits for tentacle 2D robots, we add a developmental process over the robots’ morphologies. Within a generation, the morphology of the robots does not change. But from one generation to the next, the morphology develops. Much like we become bigger, stronger, and heavier as we age, our robots are bigger, stronger, and heavier with each passing generation. Our robots start with baby morphologies, and a few thousand generations later, end-up with adult ones. We show that this produces better and qualitatively different gaits than an evolutionary search with only adult robots, and that it prevents premature convergence by fostering exploration. In addition, we validate our method on voxel lattice 3D robots from the literature and compare it to a recent evolutionary developmental approach. Our method is conceptually simple, and it can be effective on small or large populations of robots, and intrinsic to the robot and its morphology, not the task or environment. Furthermore, by recasting the evolutionary search as a learning process, these results can be viewed in the context of developmental learning robotics.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"3-21"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/iel7/6720217/9930987/09934835.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48127124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
It is already well known that environmental variation has a big effect on real evolution, and similar effects have been found in evolutionary artificial life simulations. In particular, a lot of research has been carried out on how the various evolutionary outcomes depend on the noise distributions representing the environmental changes, and how important it is for models to use inverse power-law distributions with the right noise colour. However, there are two distinct factors of relevance—the average total magnitude of change per unit time and the distribution of individual change magnitudes—and misleading results may emerge if those factors are not properly separated. This article makes use of an existing agent-based artificial life modeling framework to explore this issue using models previously tried and tested for other purposes. It begins by demonstrating how the total magnitude and distribution effects can easily be confused, and goes on to show how it is possible to untangle the influence of these interacting factors by using correlation-based normalization. It then presents a series of simulation results demonstrating that interesting dependencies on the noise distribution remain after separating those factors, but many effects involving the noise colour of inverse power-law distributions disappear, and very similar results arise across restricted-range white-noise distributions. The average total magnitude of change per unit time is found to have a substantial effect on the simulation outcomes, but the distribution of individual changes has very little effect. A robust counterexample is thereby provided to the idea that it is always important to use accurate environmental change distributions in artificial life models.
{"title":"Effect of Environmental Change Distribution on Artificial Life Simulations","authors":"John A. Bullinaria","doi":"10.1162/artl_a_00366","DOIUrl":"10.1162/artl_a_00366","url":null,"abstract":"It is already well known that environmental variation has a big effect on real evolution, and similar effects have been found in evolutionary artificial life simulations. In particular, a lot of research has been carried out on how the various evolutionary outcomes depend on the noise distributions representing the environmental changes, and how important it is for models to use inverse power-law distributions with the right noise colour. However, there are two distinct factors of relevance—the average total magnitude of change per unit time and the distribution of individual change magnitudes—and misleading results may emerge if those factors are not properly separated. This article makes use of an existing agent-based artificial life modeling framework to explore this issue using models previously tried and tested for other purposes. It begins by demonstrating how the total magnitude and distribution effects can easily be confused, and goes on to show how it is possible to untangle the influence of these interacting factors by using correlation-based normalization. It then presents a series of simulation results demonstrating that interesting dependencies on the noise distribution remain after separating those factors, but many effects involving the noise colour of inverse power-law distributions disappear, and very similar results arise across restricted-range white-noise distributions. The average total magnitude of change per unit time is found to have a substantial effect on the simulation outcomes, but the distribution of individual changes has very little effect. A robust counterexample is thereby provided to the idea that it is always important to use accurate environmental change distributions in artificial life models.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"134-153"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48786041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The emergence of sex robots raises important issues about what it means to be human and the commodification of love, companionship, and sex. This commentary discusses the following question: If some members of society relate to robots as “humans,” what does this mean for society’s conceptualisation of personhood and intimate relationships? How love is expressed between individuals is normally considered a very private expression of companionship that should remain in the private sphere. This article examines whether sex robots should be subject to public regulation given the broader societal impacts of their ability to emotionally connect and elicit empathy from humans.
{"title":"A Response to Paolo Euron’s “Uncanny Beauty: Aesthetics of Companionship, Love, and Sex Robots”","authors":"Maria O’Sullivan","doi":"10.1162/artl_a_00363","DOIUrl":"10.1162/artl_a_00363","url":null,"abstract":"The emergence of sex robots raises important issues about what it means to be human and the commodification of love, companionship, and sex. This commentary discusses the following question: If some members of society relate to robots as “humans,” what does this mean for society’s conceptualisation of personhood and intimate relationships? How love is expressed between individuals is normally considered a very private expression of companionship that should remain in the private sphere. This article examines whether sex robots should be subject to public regulation given the broader societal impacts of their ability to emotionally connect and elicit empathy from humans.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"128-133"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47305172","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Can machines ever be sentient? Could they perceive and feel things; be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path towards “General Intelligence” in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines.
{"title":"Making Artificial Brains: Components, Topology, and Optimization","authors":"Christoph Adami","doi":"10.1162/artl_a_00364","DOIUrl":"10.1162/artl_a_00364","url":null,"abstract":"Can machines ever be sentient? Could they perceive and feel things; be conscious of their surroundings? What are the prospects of achieving sentience in a machine? What are the dangers associated with such an endeavor, and is it even ethical to embark on such a path to begin with? In the series of articles of this column, I discuss one possible path towards “General Intelligence” in machines: to use the process of Darwinian evolution to produce artificial brains that can be grafted onto mobile robotic platforms, with the goal of achieving fully embodied sentient machines.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"157-166"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46283792","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We demonstrate a novel computational architecture based on fluid convection logic gates and heat flux-mediated information flows. Our previous work demonstrated that Boolean logic operations can be performed by thermally driven convection flows. In this work, we use numerical simulations to demonstrate a different , but universal Boolean logic operation (NOR), performed by simpler convective gates. The gates in the present work do not rely on obstacle flows or periodic boundary conditions, a significant improvement in terms of experimental realizability. Conductive heat transfer links can be used to connect the convective gates, and we demonstrate this with the example of binary half addition. These simulated circuits could be constructed in an experimental setting with modern, 2-dimensional fluidics equipment, such as a thin layer of fluid between acrylic plates. The presented approach thus introduces a new realm of unconventional, thermal fluid-based computation.
{"title":"Computation by Convective Logic Gates and Thermal Communication","authors":"Stuart Bartlett;Andrew K. Gao;Yuk L. Yung","doi":"10.1162/artl_a_00358","DOIUrl":"10.1162/artl_a_00358","url":null,"abstract":"We demonstrate a novel computational architecture based on fluid convection logic gates and heat flux-mediated information flows. Our previous work demonstrated that Boolean logic operations can be performed by thermally driven convection flows. In this work, we use numerical simulations to demonstrate a different , but universal Boolean logic operation (NOR), performed by simpler convective gates. The gates in the present work do not rely on obstacle flows or periodic boundary conditions, a significant improvement in terms of experimental realizability. Conductive heat transfer links can be used to connect the convective gates, and we demonstrate this with the example of binary half addition. These simulated circuits could be constructed in an experimental setting with modern, 2-dimensional fluidics equipment, such as a thin layer of fluid between acrylic plates. The presented approach thus introduces a new realm of unconventional, thermal fluid-based computation.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"96-107"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45198390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Julian’s work is well known throughout the Artificial Life community: His Cartesian genetic programming (CGP) and in materio computing are foundational concepts. He also made contributions in morphological computing and neurocomputing, all based on his fascination with evolution as a means of attacking and solving problems. Like many in the ALife community, he had an interdisciplinary career, commencing with a first degree in Physics and a PhD in Mathematics, followed by research in Natural Computing and material computing at the universities of Napier, Birmingham, and York in the UK. Julian invented CGP (Miller, 1999), a way of encoding graph programs (functional nodes connected by edges) in a string of integers, allowing the string to be evolved in the standard way, with the graph (located on a Cartesian grid, hence its name) produced as the result of a genotype to phenotype mapping. From this simple beginning, Julian and his students continued to develop the approach, and other researchers joined in. Ten years later, the field had grown significantly, with many researchers both using CGP in their own work and extending the original concept. Indeed, the field had grown enough that Julian could edit an entire book on the topic (Miller, 2011).
{"title":"Julian Francis Miller, 1955–2022","authors":"Susan Stepney;Alan Dorin","doi":"10.1162/artl_a_00371","DOIUrl":"10.1162/artl_a_00371","url":null,"abstract":"Julian’s work is well known throughout the Artificial Life community: His Cartesian genetic programming (CGP) and in materio computing are foundational concepts. He also made contributions in morphological computing and neurocomputing, all based on his fascination with evolution as a means of attacking and solving problems. Like many in the ALife community, he had an interdisciplinary career, commencing with a first degree in Physics and a PhD in Mathematics, followed by research in Natural Computing and material computing at the universities of Napier, Birmingham, and York in the UK. Julian invented CGP (Miller, 1999), a way of encoding graph programs (functional nodes connected by edges) in a string of integers, allowing the string to be evolved in the standard way, with the graph (located on a Cartesian grid, hence its name) produced as the result of a genotype to phenotype mapping. From this simple beginning, Julian and his students continued to develop the approach, and other researchers joined in. Ten years later, the field had grown significantly, with many researchers both using CGP in their own work and extending the original concept. Indeed, the field had grown enough that Julian could edit an entire book on the topic (Miller, 2011).","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"28 1","pages":"154-156"},"PeriodicalIF":2.6,"publicationDate":"2022-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47127970","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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