Towards a theory of emergent functionality
Proceedings of the first international conference on simulation of adaptive behavior on From animals to animats
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Machine Learning - Special issue on genetic algorithms
An introduction to genetic algorithms
An introduction to genetic algorithms
The Simple Genetic Algorithm: Foundations and Theory
The Simple Genetic Algorithm: Foundations and Theory
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Compositional evolution: interdisciplinary investigations in evolvability, modularity, and symbiosis
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
Intrinsic emergence boosts adaptive capacity
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
On the complexity of hierarchical problem solving
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
Evolutionary transitions as a metaphor for evolutionary optimisation
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
CoEvolution of effective observers and observed multi-agents system
ECAL'05 Proceedings of the 8th European conference on Advances in Artificial Life
Compact genetic codes as a search strategy of evolutionary processes
FOGA'05 Proceedings of the 8th international conference on Foundations of Genetic Algorithms
A gestalt genetic algorithm: less details for better search
Proceedings of the 9th annual conference on Genetic and evolutionary computation
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Many metaheuristics have difficulty exploring their search space comprehensively. Exploration time and efficiency are highly dependent on the size and the ruggedness of the search space. For instance, the Simple Genetic Algorithm (SGA) is not totally suited to traverse very large landscapes, especially deceptive ones. The approach introduced here aims at improving the exploration process of the SGA by adding a second search process through the way the solutions are coded. An "observer" is defined as each possible encoding that aims at reducing the search space. Adequacy of one observer is computed by applying this specific encoding and evaluating how this observer is beneficial for the SGA run. The observers are trained for a specific time by a second evolutionary stage. During the evolution of the observers, the most suitable observer helps the SGA to find a solution to the tackled problem faster. These observers aim at collapsing the search space and smoothing its ruggedness through a simplification of the genotype. A first implementation of this general approach is proposed, tested on the Shuffled Hierarchical IF-and-only-iF (SHIFF) problem. Very good results are obtained and some explanations are provided about why our approach tackles SHIFF so easily.