Genetic algorithms in time-dependent environments
Theoretical aspects of evolutionary computing
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Random Dynamics Optimum Tracking with Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Genetic Algorithms: Principles and Perspectives: A Guide to GA Theory
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Dynamic evolutionary optimisation: an analysis of frequency and magnitude of change
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
An analysis of the XOR dynamic problem generator based on the dynamical system
PPSN'10 Proceedings of the 11th international conference on Parallel problem solving from nature: Part I
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
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In this work, the modifications in the fitness landscape in two simple Dynamic Optimization Problems involving Evolutionary Robots are theoretically investigated. In the first dynamic problem, a robot with control laws optimized by a Genetic Algorithm should navigate in a simple environment. The changes in the fitness landscape are caused in this case by sensor faults. The second problem investigated here is when Evolutionary Robots are employed to reproduce the behaviour of rats in a maze. Changes in the rat's condition cause the modification in the fitness landscape. Simulations using the exact model of the Genetic Algorithm in each problem are presented, what allows studying the dynamical behaviour of the population of the GA, helping in the analysis of the performance obtained in practice.