Seeing the light: artificial evolution, real vision
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Automatic definition of modular neural networks
Adaptive Behavior
Discontinuity in evolution: how different levels of organization imply preadaptation
Adaptive individuals in evolving populations
ALIFE Proceedings of the sixth international conference on Artificial life
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolving neural networks through augmenting topologies
Evolutionary Computation
Simulation of Exaptive Behaviour
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Evolutionary Robotics: The Biology, Intelligence, and Technology of Self-Organizing Machines
Measuring, enabling and comparing modularity, regularity and hierarchy in evolutionary design
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Generating large-scale neural networks through discovering geometric regularities
Proceedings of the 9th annual conference on Genetic and evolutionary computation
On the hardness of offline multi-objective optimization
Evolutionary Computation
Incremental evolution of target-following neuro-controllers for flapping-wing animats
SAB'06 Proceedings of the 9th international conference on From Animals to Animats: simulation of Adaptive Behavior
IEEE Transactions on Neural Networks
Importing the computational neuroscience toolbox into neuro-evolution-application to basal ganglia
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Automatic task decomposition for the neuroevolution of augmenting topologies (NEAT) algorithm
EvoBIO'12 Proceedings of the 10th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
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Despite their success as optimization methods, evolutionary algorithms face many difficulties to design artifacts with complex structures. According to paleontologists, living organisms evolved by opportunistically co-opting characters adapted to a function to solve new problems, a phenomenon called exaptation. In this paper, we draw the hypotheses (1) that exaptation requires the presence of multiple selection pressures, (2) that Pareto-based multi-objective evolutionary algorithms (MOEA) can create such pressures and (3) that the modularity of the genotype is a key to enable exaptation. To explore these hypotheses, we designed an evolutionary process to find the structure and the parameters of neural networks to compute a Boolean function with a modular structure. We then analyzed the role of each component using a Shapley value analysis. Our results show that: (1) the proposed method is efficient to evolve neural networks to solve this task; (2) genotypic modules and multiple selections gradients needed to be aligned to converge faster than the control experiments. This prominent role of multiple selection pressures contradicts the basic assumption that underlies most published modular methods for the evolution of neural networks, in which only the modularity of the genotype is considered.