Pub Date : 2022-08-16DOI: 10.1007/s11047-022-09911-4
Leonardo Lai, Lorenzo Fiaschi, M. Cococcioni, K. Deb
{"title":"Pure and mixed lexicographic-paretian many-objective optimization: state of the art","authors":"Leonardo Lai, Lorenzo Fiaschi, M. Cococcioni, K. Deb","doi":"10.1007/s11047-022-09911-4","DOIUrl":"https://doi.org/10.1007/s11047-022-09911-4","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48699209","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}
Pub Date : 2022-08-12DOI: 10.1007/s11047-022-09914-1
Arian Allenson M. Valdez, Filbert Wee, A. N. L. Odasco, Matthew Lemuel M. Rey, F. Cabarle
{"title":"GPU simulations of spiking neural P systems on modern web browsers","authors":"Arian Allenson M. Valdez, Filbert Wee, A. N. L. Odasco, Matthew Lemuel M. Rey, F. Cabarle","doi":"10.1007/s11047-022-09914-1","DOIUrl":"https://doi.org/10.1007/s11047-022-09914-1","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44062916","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}
Pub Date : 2022-08-11DOI: 10.1007/s11047-022-09915-0
Johannes Lengler, Jonas Meier
Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the ((1+1))-Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for dynamic binval, the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for ((mu +1))-EA is not close the optimum, but far away from it. In contrast, for the ((mu +1))-GA, the region around the optimum is the hardest region in all studied cases.Kindly check and confirm the inserted city name is correctly identified.Correct.
{"title":"Large population sizes and crossover help in dynamic environments","authors":"Johannes Lengler, Jonas Meier","doi":"10.1007/s11047-022-09915-0","DOIUrl":"https://doi.org/10.1007/s11047-022-09915-0","url":null,"abstract":"<p>Dynamic linear functions on the boolean hypercube are functions which assign to each bit a positive weight, but the weights change over time. Throughout optimization, these functions maintain the same global optimum, and never have defecting local optima. Nevertheless, it was recently shown [Lengler, Schaller, FOCI 2019] that the <span>((1+1))</span>-Evolutionary Algorithm needs exponential time to find or approximate the optimum for some algorithm configurations. In this experimental paper, we study the effect of larger population sizes for <i>dynamic binval</i>, the extreme form of dynamic linear functions. We find that moderately increased population sizes extend the range of efficient algorithm configurations, and that crossover boosts this positive effect substantially. Remarkably, similar to the static setting of monotone functions in [Lengler, Zou, FOGA 2019], the hardest region of optimization for <span>((mu +1))</span>-EA is not close the optimum, but far away from it. In contrast, for the <span>((mu +1))</span>-GA, the region around the optimum is the hardest region in all studied cases.Kindly check and confirm the inserted city name is correctly identified.Correct.</p>","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513860","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}
Pub Date : 2022-08-11DOI: 10.1007/s11047-022-09916-z
R. S. Parpinelli, Nilcimar Neitzel Will, Renan Samuel da Silva
{"title":"A self-adaptive evolutionary algorithm using Monte Carlo Fragment insertion and conformation clustering for the protein structure prediction problem","authors":"R. S. Parpinelli, Nilcimar Neitzel Will, Renan Samuel da Silva","doi":"10.1007/s11047-022-09916-z","DOIUrl":"https://doi.org/10.1007/s11047-022-09916-z","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49440256","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}
Pub Date : 2022-08-10DOI: 10.1007/s11047-022-09897-z
Gabriel Ciobanu, E. Todoran
{"title":"Spiking neural P systems and their semantics in Haskell","authors":"Gabriel Ciobanu, E. Todoran","doi":"10.1007/s11047-022-09897-z","DOIUrl":"https://doi.org/10.1007/s11047-022-09897-z","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48940562","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}
Pub Date : 2022-07-27DOI: 10.1007/s11047-022-09889-z
Shuwei Zhu, Lihong Xu, E. Goodman, K. Deb, Zhichao Lu
{"title":"A general framework for enhancing relaxed Pareto dominance methods in evolutionary many-objective optimization","authors":"Shuwei Zhu, Lihong Xu, E. Goodman, K. Deb, Zhichao Lu","doi":"10.1007/s11047-022-09889-z","DOIUrl":"https://doi.org/10.1007/s11047-022-09889-z","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49367340","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}
Pub Date : 2022-07-26DOI: 10.1007/s11047-022-09898-y
T. Gwizdalla, L. Manzoni, G. Mauri
{"title":"Preface","authors":"T. Gwizdalla, L. Manzoni, G. Mauri","doi":"10.1007/s11047-022-09898-y","DOIUrl":"https://doi.org/10.1007/s11047-022-09898-y","url":null,"abstract":"","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47569418","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}
Pub Date : 2022-07-26DOI: 10.1007/s11047-022-09904-3
Kamil Khadiev, Aliya Khadieva, Alexander Knop
In this paper, we study quantum Ordered Binary Decision Diagrams((mathrm {OBDD})) model; it is a restricted version of read-once quantum branching programs, with respect to “width” complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique (“reordering”) for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function (mathrm {REQ}) such that the deterministic (mathrm {OBDD}) complexity of it is at least (2^{varOmega (n / log n)}), and the quantum (mathrm {OBDD}) complexity of it is at most (O(n^2/log n)). It is the biggest known gap for explicit functions not representable by (mathrm {OBDD})s of a linear width. Another function(shifted equality function) allows us to obtain a gap (2^{varOmega (n)}) vs (O(n^2)). Moreover, we prove the bounded error quantum and probabilistic (mathrm {OBDD}) width hierarchies for complexity classes of Boolean functions. Additionally, using “reordering” method we extend a hierarchy for read-k-times Ordered Binary Decision Diagrams (({textit{k}}text {-}mathrm {OBDD})) of polynomial width, for (k = o(n / log ^3 n)). We prove a similar hierarchy for bounded error probabilistic ({textit{k}}text {-}mathrm {OBDD})s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017)
{"title":"Exponential separation between quantum and classical ordered binary decision diagrams, reordering method and hierarchies","authors":"Kamil Khadiev, Aliya Khadieva, Alexander Knop","doi":"10.1007/s11047-022-09904-3","DOIUrl":"https://doi.org/10.1007/s11047-022-09904-3","url":null,"abstract":"<p>In this paper, we study quantum Ordered Binary Decision Diagrams(<span>(mathrm {OBDD})</span>) model; it is a restricted version of read-once quantum branching programs, with respect to “width” complexity. It is known that the maximal gap between deterministic and quantum complexities is exponential. But there are few examples of functions with such a gap. We present a new technique (“reordering”) for proving lower bounds and upper bounds for OBDD with an arbitrary order of input variables if we have similar bounds for the natural order. Using this transformation, we construct a total function <span>(mathrm {REQ})</span> such that the deterministic <span>(mathrm {OBDD})</span> complexity of it is at least <span>(2^{varOmega (n / log n)})</span>, and the quantum <span>(mathrm {OBDD})</span> complexity of it is at most <span>(O(n^2/log n))</span>. It is the biggest known gap for explicit functions not representable by <span>(mathrm {OBDD})</span>s of a linear width. Another function(shifted equality function) allows us to obtain a gap <span>(2^{varOmega (n)})</span> vs <span>(O(n^2))</span>. Moreover, we prove the bounded error quantum and probabilistic <span>(mathrm {OBDD})</span> width hierarchies for complexity classes of Boolean functions. Additionally, using “reordering” method we extend a hierarchy for read-<i>k</i>-times Ordered Binary Decision Diagrams (<span>({textit{k}}text {-}mathrm {OBDD})</span>) of polynomial width, for <span>(k = o(n / log ^3 n))</span>. We prove a similar hierarchy for bounded error probabilistic <span>({textit{k}}text {-}mathrm {OBDD})</span>s of polynomial, superpolynomial and subexponential width. The extended abstract of this work was presented on International Computer Science Symposium in Russia, CSR 2017, Kazan, Russia, June 8 – 12, 2017 Khadiev and Khadieva (2017)</p>","PeriodicalId":49783,"journal":{"name":"Natural Computing","volume":null,"pages":null},"PeriodicalIF":2.1,"publicationDate":"2022-07-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138513862","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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