Matthew Andres Moreno;Santiago Rodriguez-Papa;Emily Dolson
Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure; (b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure; and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers’ phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile tool kit with which to study large-scale, evolving populations.
{"title":"Ecology, Spatial Structure, and Selection Pressure Induce Strong Signatures in Phylogenetic Structure","authors":"Matthew Andres Moreno;Santiago Rodriguez-Papa;Emily Dolson","doi":"10.1162/artl_a_00470","DOIUrl":"10.1162/artl_a_00470","url":null,"abstract":"Evolutionary dynamics are shaped by a variety of fundamental, generic drivers, including spatial structure, ecology, and selection pressure. These drivers impact the trajectory of evolution and have been hypothesized to influence phylogenetic structure. For instance, they can help explain natural history, steer behavior of contemporary evolving populations, and influence the efficacy of application-oriented evolutionary optimization. Likewise, in inquiry-oriented Artificial Life systems, these drivers constitute key building blocks for open-ended evolution. Here we set out to assess (a) if spatial structure, ecology, and selection pressure leave detectable signatures in phylogenetic structure; (b) the extent, in particular, to which ecology can be detected and discerned in the presence of spatial structure; and (c) the extent to which these phylogenetic signatures generalize across evolutionary systems. To this end, we analyze phylogenies generated by manipulating spatial structure, ecology, and selection pressure within three computational models of varied scope and sophistication. We find that selection pressure, spatial structure, and ecology have characteristic effects on phylogenetic metrics, although these effects are complex and not always intuitive. Signatures have some consistency across systems when using equivalent taxonomic unit definitions (e.g., individual, genotype, species). Furthermore, we find that sufficiently strong ecology can be detected in the presence of spatial structure. We also find that, while low-resolution phylogenetic reconstructions can bias some phylogenetic metrics, high-resolution reconstructions recapitulate them faithfully. Although our results suggest a potential for evolutionary inference of spatial structure, ecology, and selection pressure through phylogenetic analysis, further methods development is needed to distinguish these drivers’ phylometric signatures from each other and to appropriately normalize phylogenetic metrics. With such work, phylogenetic analysis could provide a versatile tool kit with which to study large-scale, evolving populations.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"31 2","pages":"129-152"},"PeriodicalIF":1.6,"publicationDate":"2025-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144053345","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 predominant explanations for including chromosomal recombination during meiosis are that it serves as a mechanism for repair or as a mechanism for increased adaptability. However, neither gives a clear immediate selective advantage to the reproducing organism itself. This letter revisits the idea that sex emerged and is maintained because it enables a simple form of fitness landscape smoothing to explain why recombination evolved. Although recombination was originally included in the idea, as with the other explanations, no immediate benefit was identified. That a benefit exists if the dividing cell(s) form a simple colony of the resulting haploids for some time after reproduction is explored here and shown to further increase the benefits of the landscape smoothing process.
{"title":"On Recombination","authors":"Larry Bull","doi":"10.1162/artl_a_00453","DOIUrl":"10.1162/artl_a_00453","url":null,"abstract":"The predominant explanations for including chromosomal recombination during meiosis are that it serves as a mechanism for repair or as a mechanism for increased adaptability. However, neither gives a clear immediate selective advantage to the reproducing organism itself. This letter revisits the idea that sex emerged and is maintained because it enables a simple form of fitness landscape smoothing to explain why recombination evolved. Although recombination was originally included in the idea, as with the other explanations, no immediate benefit was identified. That a benefit exists if the dividing cell(s) form a simple colony of the resulting haploids for some time after reproduction is explored here and shown to further increase the benefits of the landscape smoothing process.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"442-447"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407254","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}
On this 30th anniversary of the founding of the Artificial Life journal, I share some personal reflections on my own history of engagement with the field, my own particular assessment of its current status, and my vision for its future development. At the very least, I hope to stimulate some necessary critical conversations about the field of Artificial Life and where it is going.
{"title":"(A)Life as It Could Be","authors":"Randall D. Beer","doi":"10.1162/artl_a_00455","DOIUrl":"10.1162/artl_a_00455","url":null,"abstract":"On this 30th anniversary of the founding of the Artificial Life journal, I share some personal reflections on my own history of engagement with the field, my own particular assessment of its current status, and my vision for its future development. At the very least, I hope to stimulate some necessary critical conversations about the field of Artificial Life and where it is going.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"539-545"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407251","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}
{"title":"Comment on Randall D. Beer’s “A(Life) as It Could Be”","authors":"Inman Harvey","doi":"10.1162/artl_a_00456","DOIUrl":"10.1162/artl_a_00456","url":null,"abstract":"","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"546-547"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407252","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}
Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.
{"title":"How Perception, Actuation, and Communication Impact the Emergence of Collective Intelligence in Simulated Modular Robots","authors":"Francesco Rusin;Eric Medvet","doi":"10.1162/artl_a_00447","DOIUrl":"10.1162/artl_a_00447","url":null,"abstract":"Modular robots are collections of simple embodied agents, the modules, that interact with each other to achieve complex behaviors. Each module may have a limited capability of perceiving the environment and performing actions; nevertheless, by behaving coordinately, and possibly by sharing information, modules can collectively perform complex actions. In principle, the greater the actuation, perception, and communication abilities of the single module are the more effective is the collection of modules. However, improved abilities also correspond to more complex controllers and, hence, larger search spaces when designing them by means of optimization. In this article, we analyze the impact of perception, actuation, and communication abilities on the possibility of obtaining good controllers for simulated modular robots, that is, controllers that allow the robots to exhibit collective intelligence. We consider the case of modular soft robots, where modules can contract, expand, attach, and detach from each other, and make them face two tasks (locomotion and piling), optimizing their controllers with evolutionary computation. We observe that limited abilities often do not prevent the robots from succeeding in the task, a finding that we explain with (a) the smaller search space corresponding to limited actuation, perception, and communication abilities, which makes the optimization easier, and (b) the fact that, for this kind of robot, morphological computation plays a significant role. Moreover, we discover that what matters more is the degree of collectivity the robots are required to exhibit when facing the task.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"448-465"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992506","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}
Then knowledge is to be found not in the experiences but in the process of reasoning about them; it is here, seemingly, not in the experiences, that it is possible to grasp being and truth.Plato, Theaetetus 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 toward “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":"How Brains Perceive the World","authors":"Christoph Adami","doi":"10.1162/artl_a_00454","DOIUrl":"10.1162/artl_a_00454","url":null,"abstract":"Then knowledge is to be found not in the experiences but in the process of reasoning about them; it is here, seemingly, not in the experiences, that it is possible to grasp being and truth.Plato, Theaetetus 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 toward “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":"30 4","pages":"551-563"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142407253","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}
Threshold models in which an individual’s response to a particular state of the world depends on whether an associated measured value exceeds a given threshold are common in a variety of social learning and collective decision-making scenarios in both natural and artificial systems. If thresholds are heterogeneous across a population of agents, then graded population level responses can emerge in a context in which individual responses are discrete and limited. In this article, I propose a threshold-based model for social learning of shared quality categories. This is then combined with the voting model of fuzzy categories to allow individuals to learn membership functions from their peers, which can then be used for decision-making, including ranking a set of available options. I use agent-based simulation experiments to investigate variants of this model and compare them to an individual learning benchmark when applied to the ranking problem. These results show that a threshold-based approach combined with category-based voting across a social network provides an effective social mechanism for ranking that exploits emergent vagueness.
{"title":"Heterogeneous Thresholds, Social Ranking, and the Emergence of Vague Categories","authors":"Jonathan Lawry","doi":"10.1162/artl_a_00442","DOIUrl":"10.1162/artl_a_00442","url":null,"abstract":"Threshold models in which an individual’s response to a particular state of the world depends on whether an associated measured value exceeds a given threshold are common in a variety of social learning and collective decision-making scenarios in both natural and artificial systems. If thresholds are heterogeneous across a population of agents, then graded population level responses can emerge in a context in which individual responses are discrete and limited. In this article, I propose a threshold-based model for social learning of shared quality categories. This is then combined with the voting model of fuzzy categories to allow individuals to learn membership functions from their peers, which can then be used for decision-making, including ranking a set of available options. I use agent-based simulation experiments to investigate variants of this model and compare them to an individual learning benchmark when applied to the ranking problem. These results show that a threshold-based approach combined with category-based voting across a social network provides an effective social mechanism for ranking that exploits emergent vagueness.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"523-538"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141443803","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}
Rahul Dubey;Simon Hickinbotham;Andrew Colligan;Imelda Friel;Edgar Buchanan;Mark Price;Andy M. Tyrrell
Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value–encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.
{"title":"Evolving Novel Gene Regulatory Networks for Structural Engineering Designs","authors":"Rahul Dubey;Simon Hickinbotham;Andrew Colligan;Imelda Friel;Edgar Buchanan;Mark Price;Andy M. Tyrrell","doi":"10.1162/artl_a_00448","DOIUrl":"10.1162/artl_a_00448","url":null,"abstract":"Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value–encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.","PeriodicalId":55574,"journal":{"name":"Artificial Life","volume":"30 4","pages":"466-485"},"PeriodicalIF":1.6,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141992505","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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