Experimental results on the crossover point in random 3-SAT
Artificial Intelligence - Special volume on frontiers in problem solving: phase transitions and complexity
Emergence of Artificial Cognition: An Introduction to Collective Learning
Emergence of Artificial Cognition: An Introduction to Collective Learning
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
The Density of States - A Measure of the Difficulty of Optimisation Problems
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
Intelligent recombination using genotypic learning in a collective learning genetic algorithm
Intelligent recombination using genotypic learning in a collective learning genetic algorithm
Predicting epistasis from mathematical models
Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
A survey of techniques for characterising fitness landscapes and some possible ways forward
Information Sciences: an International Journal
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Fitness landscape complexity in the context of evolutionary algorithms can be considered to be a relative term due to the complex interaction between search strategy, problem difficulty and problem representation. A new paradigm for genetic search referred to as the Collective Learning Genetic Algorithm (CLGA) has been demonstrated for combinatorial optimization problems which utilizes genotypic learning to do recombination based on a cooperative exchange of knowledge (instead of symbols) between interacting chromosomes. There is evidence to suggest that the CLGA is able to modify its recombinative behavior based on the consistency of the information in its environment, specifically, the observed fitness landscape. By analyzing the structure of the evolving individuals, a landscape-complexity measure is extracted a posteriori and then plotted for various types of example problems. This paper presents preliminary results that show that the CLGA appears to adapt its search strategy to the fitness landscape induced by the CLGA itself, and hence relative to the landscape being searched.