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
Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Robotics: A Survey of Applications and Problems
Proceedings of the First European Workshop on Evolutionary Robotics
Evolving non-Trivial Behaviors on Real Robots: an Autonomous Robot that Picks up Objects
AI*IA '95 Proceedings of the 4th Congress of the Italian Association for Artificial Intelligence on Topics in Artificial Intelligence
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
Encouraging behavioral diversity in evolutionary robotics: An empirical study
Evolutionary Computation
Policy transfer in mobile robots using neuro-evolutionary navigation
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Bootstrapping aggregate fitness selection with evolutionary multi-objective optimization
PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part II
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Evolutionary algorithms have been successfully used to create controllers for many animats. However, intuitive fitness functions like the survival time of the animat, often do not lead to interesting results because of the bootstrap problem, arguably one of the main challenges in evolutionary robotics: if all the individuals perform equally poorly, the evolutionary process cannot start. To overcome this problem, many authors defined ordered sub-tasks to bootstrap the process, leading to an incremental evolution scheme. Published methods require a deep knowledge of the underlying structure of the analyzed task, which is often not available to the experimenter. In this paper, we propose a new incremental scheme based on multi-objective evolution. This process is able to automatically switch between each sub-task resolution and does not require to order them. The proposed method has been successfully tested on the evolution of a neuro-controller for a complex-light seeking simulated robot, involving 8 sub-tasks.