Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Punctuated equilibria: a parallel genetic algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
An investigation of niche and species formation in genetic function optimization
Proceedings of the third international conference on Genetic algorithms
Distributed genetic algorithms
Proceedings of the third international conference on Genetic algorithms
Noise, sampling, and efficient genetic algorthms
Noise, sampling, and efficient genetic algorthms
2+p-SAT: relation of typical-case complexity to the nature of the phase transition
Random Structures & Algorithms - Special issue on statistical physics methods in discrete probability, combinatorics, and theoretical computer science
Neutrality in fitness landscapes
Applied Mathematics and Computation
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Fitness Distance Correlation as a Measure of Problem Difficulty for Genetic Algorithms
Proceedings of the 6th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
The Effect of Spin-Flip Symmetry on the Performance of the Simple GA
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
From Twomax To The Ising Model: Easy And Hard Symmetrical Problems
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
The Phase-Transition Niche for Evolutionary Algorithms in Timetabling
Selected papers from the First International Conference on Practice and Theory of Automated Timetabling
Some Observations about GA-Based Exam Timetabling
PATAT '97 Selected papers from the Second International Conference on Practice and Theory of Automated Timetabling II
Adaptive Penalties for Evolutionary Graph Coloring
AE '97 Selected Papers from the Third European Conference on Artificial Evolution
Information Characteristics and the Structure of Landscapes
Evolutionary Computation
A comparison of predictive measures of problem difficulty inevolutionary algorithms
IEEE Transactions on Evolutionary Computation
Crossover is provably essential for the Ising model on trees
GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
The one-dimensional Ising model: mutation versus recombination
Theoretical Computer Science
Properties of symmetric fitness functions
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Using over-sampling in a Bayesian classifier EDA to solve deceptive and hierarchical problems
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Normalization in genetic algorithms
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Symmetry at the genotypic level and the simple inversion operator
EPIA'07 Proceedings of the aritficial intelligence 13th Portuguese conference on Progress in artificial intelligence
Quotient geometric crossovers and redundant encodings
Theoretical Computer Science
Energy landscapes of atomic clusters as black box optimization benchmarks
Evolutionary Computation
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In the context of optimization by evolutionary algorithms (EAs), epistasis, deception, and scaling are well-known examples of problem difficulty characteristics. The presence of one such characteristic in the representation of a search problem indicates a certain type of difficulty the EA is to encounter during its search for globally optimal configurations. In this paper, we claim that the occurrence of symmetry in the representation is another problem difficulty characteristic and discuss one particular form, spin-flip symmetry, characterized by fitness invariant permutations on the alphabet. Its usual effect on unspecialized EAs, premature convergence due to synchronization problems, is discussed in detail. We discuss five different ways to specialize EAs to cope with the symmetry: adapting the genetic operators, changing the fitness function, using a niching technique, using a distributed EA, and attaching a highly redundant genotype-phenotype mapping.