Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution

Alfonseca, Manuel; Gil, Francisco José Soler

doi:10.1002/cplx.21507

Cited by 13 publications

(4 citation statements)

References 22 publications

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“…They model predators and prey and observed the spontaneous development of complex schooling behavior without explicitly defining values for schooling in the fitness function. Alfonseca and Gil (2015) described another predator-prey ecosystem. Their agents ("artificial beings") were comprised of mathematical fitness functions that evolved using grammatical evolution to form different niches in a nonspatial environment with limited resources.…”

Section: Nature-inspired Evolutionary Ecosystemsmentioning

confidence: 99%

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From evolutionary ecosystem simulations to computational models of human behavior

Bentley

Lim

2022

WIRES Cognitive Science

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We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed.

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Section: Nature-inspired Evolutionary Ecosystemsmentioning

confidence: 99%

“…Alfonseca and Gil ( 2015 ) described another predator–prey ecosystem. Their agents (“artificial beings”) were comprised of mathematical fitness functions that evolved using grammatical evolution to form different niches in a nonspatial environment with limited resources.…”

Section: Introductionmentioning

confidence: 99%

From evolutionary ecosystem simulations to computational models of human behavior

Bentley

Lim

2022

WIRES Cognitive Science

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“…O 'Neill andBrabazon (2004, 2006) have successfully applied GE to predicting corporate bankruptcy, forecasting stock Índices, bond credit ratings and other flnancial applications. GE has also been used with a classic predator-prey model (Grimme and Schmitt 2006) to explore the impact of parameters such as predator efflciency, niche number and random mutations on ecological stability (Alfonseca and Soler Gil 2015). It is possible to structure a GE grammar that for a given function/terminal set is equivalent to genetic programming.…”

Section: Grammatical Evolutionmentioning

confidence: 99%

Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm)

2020

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Ant colony systems have been widely employed in optimization issues primarily focused on path flnding optimization, such as travelling salesman problem. The main advantage lies in the choice of the edge to be explored, deflned using pheromone trails. This paper proposes the use of ant colony systems to explore a Backus-Naur form grammar whose elements are solutions to a given problem. Similar models, without using ant colonies, have been used to solve optimization problems or to automatically genérate programs such as grammatical swarm (based on particle swarm optimization) and grammatical evolution (based on genetic algorithms). This workpresents the application of proposed ant colony rule derivation algorithm and benchmarks this novel approach in a well-known deceptive problem, the Santa Fe Trail. Proposed algorithm opens the way to a new branch of research in swarm intelligence, which until now has been almost nonexistent, using ant colony algorithms to genérate solutions of a given problem described by a BNF grammar with the advantage of genotype/phenotype mapping, described in grammatical evolution. In this case, such mapping is performed based on the pheromone concentration for each production rule. The experimental results demónstrate proposed algorithm outperforms grammatical evolution algorithm in the Santa Fe Trail problem with higher success rates and better solutions in terms of the required steps.

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“…Alfonseca e Gil [4] evoluíram um ecossistema de expressões matemáticas utilizando GE. Ao realizar a evolução de novas expressões, este sistema não leva em conta características espaciais associadas aos indivíduos do sistema.…”

Section: Características De Interesseunclassified

Evocycle

dos Santos Pinto de Andrade

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AgradecimentosO período de doutoramento foi uma jornada que não seria possível de ser finalizada sem a contribuição de muitas pessoas que atravessaram a minha vida. Por este motivo, devo sinceros agradecimentos a muitas dessas pessoas.Em primeiro lugar, gostaria de agradecer meu orientador, Ricardo da Silva Torres, que acreditou em mim desde começo quando começamos a trabalhar juntos com uma iniciação científica, ainda na minha graduação. Se hoje esse doutorado está completo, é por ter acreditado em mim. Obrigado! Agradeço também ao professor Claus Aranha, da universidade de Tsukuba, onde passei um período, o chamado "doutorado sanduíche". Além da grande contribuição na pesquisa, também foi a oportunidade de realizar um sonho de conhecer o Japão. Por toda a ajuda, obrigado! Agradeço também a minha família, meus pais Carmén e Adauto, e minha irmã Vanessa, meu irmão Kauê e todos os outros membros da família, pelo apoio e estrutura na vida. Obrigado! Também mando meus agradecimentos a todos os amigos,

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Evolving a predator–prey ecosystem of mathematical expressions with grammatical evolution

Cited by 13 publications

References 22 publications

From evolutionary ecosystem simulations to computational models of human behavior

From evolutionary ecosystem simulations to computational models of human behavior

Ant colony systems optimization applied to BNF grammars rule derivation (ACORD algorithm)

Evocycle

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