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Evolving Morphological and Behavioral Diversity Without Predefined Behavior Primitives

机译:没有预先定义的行为基元的不断变化的形态和行为多样性

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Virtual ecosystems, where natural selection is used to evolve complex agent behavior, are often preferred to traditional genetic algorithms because the absence of an explicitly defined fitness allows for a less constrained evolutionary process. However, these model ecosystems typically pre-specify a discrete set of possible action primitives the agents can perform. We think that this also constrains the evolutionary process with the modellers preconceptions of what possible solutions could be. Therefore, we propose an ecosystem model to evolve complete agents where all higher-level behavior results strictly from the interplay between extremely simple components and where no 'behavior primitives' are defined. On the basis of four distinct survival strategies we show that such primitives are not necessary to evolve behavioral diversity even in a simple and homogeneous environment.
机译:传统的遗传算法通常首选使用自然选择来演化复杂的代理行为的虚拟生态系统,因为缺少明确定义的适应度可以减少约束性的进化过程。但是,这些模型生态系统通常会预先指定代理可以执行的一组离散的可能的操作原语。我们认为这也限制了建模者对可能的解决方案可能会产生的先入之见。因此,我们提出了一个生态系统模型来发展完整的代理,其中所有更高级别的行为严格地源于极其简单的组件之间的相互作用,并且没有定义“行为原语”。基于四种不同的生存策略,我们表明,即使在简单而同质的环境中,也不需要这种原始元素来演化行为多样性。

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