Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Sorting by reversals is difficult
RECOMB '97 Proceedings of the first annual international conference on Computational molecular biology
Swarm intelligence
Foundations of genetic programming
Foundations of genetic programming
Journal of Global Optimization
Uniform Crossover in Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
A Metric for Genetic Programs and Fitness Sharing
Proceedings of the European Conference on Genetic Programming
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Differential Evolution: A Practical Approach to Global Optimization (Natural Computing Series)
Geometric particle swarm optimization for the sudoku puzzle
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Geometric particle swarm optimization
Journal of Artificial Evolution and Applications - Particle Swarms: The Second Decade
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Differential Evolution: A Handbook for Global Permutation-Based Combinatorial Optimization
Geometric differential evolution
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Inbreeding properties of geometric crossover and non-geometric recombinations
FOGA'07 Proceedings of the 9th international conference on Foundations of genetic algorithms
Geometric particle swarm optimisation
EuroGP'07 Proceedings of the 10th European conference on Genetic programming
PPSN'06 Proceedings of the 9th international conference on Parallel Problem Solving from Nature
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Geometric differential evolution GDE is a recently introduced formal generalization of traditional differential evolution DE that can be used to derive specific differential evolution algorithms for both continuous and combinatorial spaces retaining the same geometric interpretation of the dynamics of the DE search across representations. In this article, we first review the theory behind the GDE algorithm, then, we use this framework to formally derive specific GDE for search spaces associated with binary strings, permutations, vectors of permutations and genetic programs. The resulting algorithms are representation-specific differential evolution algorithms searching the target spaces by acting directly on their underlying representations. We present experimental results for each of the new algorithms on a number of well-known problems comprising NK-landscapes, TSP, and Sudoku, for binary strings, permutations, and vectors of permutations. We also present results for the regression, artificial ant, parity, and multiplexer problems within the genetic programming domain. Experiments show that overall the new DE algorithms are competitive with well-tuned standard search algorithms.