From evolutionary computation to the evolution of things

Eiben, A. E.; Smith, James E.

doi:10.1038/nature14544

Cited by 395 publications

(211 citation statements)

References 87 publications

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“…But to our knowledge, interactive thread development is the first physically embodied EO used in the evolution of physically embodied robots. Thus the developmental engine of ER (Eiben et al, 2010) has its first physical instantiation, albeit a simple one; physical instantiation is a necessary condition for the complete life cycle of the evolution of things (Eiben and Smith, 2015) and in the physical evolution of ontogenies (Northcutt, 2002).…”

Section: Discussionmentioning

confidence: 99%

“…For roboticists, the evo-devo challenge is to create physically embodied systems that incorporate the three scales of time and the processes inherent in each: behavior, development, and evolution (Pfeifer and Bongard, 2006). Because of the complexity of building and evolving physical robots, this is a daunting challenge in the quest for the "evolution of things" (Eiben and Smith, 2015). As an initial step toward this goal, in this paper we create a physically embodied system that allows us to examine systematically how developmental and evolutionary processes interact.…”

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confidence: 99%

“…The main advantage of these developmental systems is that they efficiently encode phenotypes by doing so indirectly (Eiben and Smith, 2015). Genes code for processes that build structures rather than for the structures themselves.…”

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confidence: 99%

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Epigenetic Operators and the Evolution of Physically Embodied Robots

et al. 2017

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The genetic operators (GOs) of recombination, mutation, and selection are commonly included in studies of evolution and evolvability, but they are not the only operators that can affect the genotype-to-phenotype (G → P) map and thus the outcomes of evolution. In this paper, we present experiments with an epigenetic operator (EO), interactive wiring of a circuit, alongside common GOs, investigating both epigenetic and GO effects on the evolution of both simulated and physically embodied Braitenberg-inspired robots. As a platform for our experiments, we built a system that encoded the genetics for the physical circuitry of the analog robots and made explicit rules for how that circuitry would be constructed; phenotypic expression consisted of the placement of wires to form the circuitry and thus govern robot behavior. We then varied the presence of gene interactions across populations of robots, studying how the EO-and its effects on G → P maps-affected the results of evolution over several generations. Additionally, a variant of these experiments was run in simulation to provide an independent test of the evolutionary impact of this EO. Our results demonstrate that robot populations with the EO had quantitatively different and potentially less adaptive evolution than populations without it. For example, selection increased the rate at which functional circuitry was lost in the population with the EO, compared to the population without it. In addition, in simulation, EO populations were significantly less fit than populations without it. More generally, results such as these demonstrate the interaction of genetic and EOs during evolution, suggesting the broad importance of including EOs in investigations of evolvability. To our knowledge, our work represents the first physically embodied EO to be used in the evolution of physically embodied robots.

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Section: Discussionmentioning

confidence: 99%

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confidence: 99%

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Epigenetic Operators and the Evolution of Physically Embodied Robots

et al. 2017

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“…In the computational process, artificial neural networks and evolutionary computation were applied in analyzing the experimental data. The artificial neural networks are inspired by the biological neural networks within the brains of animals and have been widely applied to learning and prediction tasks,22 while the evolutionary computation is inspired by natural evolution and has been widely applied to optimization tasks 23. First, a computational model is formulated to demonstrate the relationship between the experimental parameters and objectives.…”

Section: Introductionmentioning

confidence: 99%

Computational Intelligence‐Assisted Understanding of Nature‐Inspired Superhydrophobic Behavior

et al. 2017

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In recent years, state‐of‐the‐art computational modeling of physical and chemical systems has shown itself to be an invaluable resource in the prediction of the properties and behavior of functional materials. However, construction of a useful computational model for novel systems in both academic and industrial contexts often requires a great depth of physicochemical theory and/or a wealth of empirical data, and a shortage in the availability of either frustrates the modeling process. In this work, computational intelligence is instead used, including artificial neural networks and evolutionary computation, to enhance our understanding of nature‐inspired superhydrophobic behavior. The relationships between experimental parameters (water droplet volume, weight percentage of nanoparticles used in the synthesis of the polymer composite, and distance separating the superhydrophobic surface and the pendant water droplet in adhesive force measurements) and multiple objectives (water droplet contact angle, sliding angle, and adhesive force) are built and weighted. The obtained optimal parameters are consistent with the experimental observations. This new approach to materials modeling has great potential to be applied more generally to aid design, fabrication, and optimization for myriad functional materials.

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“…Testes preliminares mostraram o comportamento de superfícies de erro e as dificuldades para o método de otimização alcançar o mínimo global (FUKUSHIMA; ROLNIK, 2017). Dando continuidade, neste trabalho utilizamos os Algoritmos Genéticos (AGs), uma metaheurística populacional que tem sido utilizada comêxito em diversos problemas (EIBEN;SMITH, 2015). Os AGs utilizam conceitos provenientes do princípio Darwiniano de seleção natural para resolver uma série de problemas (HOLLAND,1975), em especial de otimização.…”

Section: Introductionunclassified

Determinação dos saltos em equações diferenciais impulsivas

Fukushima¹,

Rolnik²,

Tinós³

2017

C.Q.D.

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Determinação dos saltos em equações diferenciais impulsivasDetermination of the jumps in impulsive differential equations Resumo As equações diferenciais impulsivas (EDIs) modelam vários problemas que surgem naturalmente em diversasáreas da ciência, sendo responsáveis por modelar fenômenos com impulsos. Nosso interesse nãoé encontrar a solução da EDI e sim determinar o salto que a solução sofre em cada momento de impulso. Os parâmetros que definem os saltos não podem ser encontrados diretamente por meio de resolução convencional. Dessa forma, tratamos o problema como inverso e adotamos uma abordagem funcional na qual procuramos minimizar um erro que confronta dois modelos. Um dos modelosé numérico, implementado computacionalmente e que utiliza valores aproximados para o salto, e outró e experimental, que corresponde aos valores das soluções que são medidas ou inferidas no tempo final do experimento. O erroé minimizado por meio de um algoritmo genético. Como aplicação, descrevemos uma situação na qual o salto correspondeà dosagem de um medicamento. O programa desenvolvido encontrou com um alto grau de precisão, a solução do problema proposto, o que nos permite concluir que a abordagemé viável. Palavras-chave: Problemas inversos. Equações diferenciais impulsivas. Determinação do impulso. Algoritmos genéticos. AbstractImpulsive Differential Equations (IDEs) model several problems that arise naturally in several areas of science and are responsible for modelling phenomena with impulses. Our interest is not to find the solution of the IDE but to determine the jump suffered in each impulsive moment. The parameters of the jump cannot be found directly by conventional resolution. In this way, we treat the problem as inverse and we adopt a functional approach to minimize an error that confronts two models. One of the models is numerical, implemented computationally and that uses approximate values for the jump, and the other one is experimental, that corresponds to the values of the solutions that are measured or inferred in the end of the experiment. The error is minimized by genetic algorithm. As an application, we describe a situation in which the jump variation corresponds to the dosage of a drug. The developed program found with a high degree of precision the solution of the proposed problem, which allows us to conclude that the approach is feasible.

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From evolutionary computation to the evolution of things

Cited by 395 publications

References 87 publications

Epigenetic Operators and the Evolution of Physically Embodied Robots

Epigenetic Operators and the Evolution of Physically Embodied Robots

Computational Intelligence‐Assisted Understanding of Nature‐Inspired Superhydrophobic Behavior

Determinação dos saltos em equações diferenciais impulsivas

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