Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Learning and evolution in neural networks
Adaptive Behavior
Autonomous Robots
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Genetic Programming IV: Routine Human-Competitive Machine Intelligence
Evolutionary Function Approximation for Reinforcement Learning
The Journal of Machine Learning Research
Design of evolvable computer languages
IEEE Transactions on Evolutionary Computation
Cooperative network construction using digital germlines
Proceedings of the 10th annual conference on Genetic and evolutionary computation
Evolution of robust data distribution among digital organisms
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
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In the natural world, individual organisms can adapt as their environment changes. In most in silico evolution, however, individual organisms tend to consist of rigid solutions, with all adaptation occurring at the population level. If we are to use artificial evolving systems as a tool in understanding biology or in engineering robust and intelligent systems, however, they should be able to generate solutions with fitness-enhancing phenotypic plasticity. Here we use Avida, an established digital evolution system, to investigate the selective pressures that produce phenotypic plasticity. We witness two different types of fitness-enhancing plasticity evolve: static-execution-flow plasticity, in which the same sequence of actions produces different results depending on the environment, and dynamic-execution-flow plasticity, where organisms choose their actions based on their environment. We demonstrate that the type of plasticity that evolves depends on the environmental challenge the population faces. Finally, we compare our results to similar ones found in vastly different systems, which suggest that this phenomenon is a general feature of evolution.