Combinatorial optimization: algorithms and complexity
Combinatorial optimization: algorithms and complexity
Empirical methods for artificial intelligence
Empirical methods for artificial intelligence
Niching methods for genetic algorithms
Niching methods for genetic algorithms
Meta-Heuristics: Theory and Applications
Meta-Heuristics: Theory and Applications
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
The Design of Innovation: Lessons from and for Competent Genetic Algorithms
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Metamodel-Assisted Evolution Strategies
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
On the "Explorative Power" of ES/EP-like Algorithms
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Using approximations to accelerate engineering design optimization
Using approximations to accelerate engineering design optimization
Metaheuristics in combinatorial optimization: Overview and conceptual comparison
ACM Computing Surveys (CSUR)
A multiple-population evolutionary approach to gate matrix layout
International Journal of Systems Science
A comprehensive survey of fitness approximation in evolutionary computation
Soft Computing - A Fusion of Foundations, Methodologies and Applications
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Optimal contraction theorem for exploration-exploitation tradeoff in search and optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Continuous non-revisiting genetic algorithm
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Analysis of a simple evolutionary algorithm for minimization in euclidean spaces
ICALP'03 Proceedings of the 30th international conference on Automata, languages and programming
Search intensity versus search diversity: a false trade off?
Applied Intelligence
Review of meta-heuristics and generalised evolutionary walk algorithm
International Journal of Bio-Inspired Computation
Why and how to measure exploration in behavioral space
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Inertia Weight Particle Swarm Optimization with Boltzmann Exploration
CIS '11 Proceedings of the 2011 Seventh International Conference on Computational Intelligence and Security
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
The exploration/exploitation tradeoff in dynamic cellular genetic algorithms
IEEE Transactions on Evolutionary Computation
On Evolutionary Exploration and Exploitation
Fundamenta Informaticae
GPU Computing for Parallel Local Search Metaheuristic Algorithms
IEEE Transactions on Computers
Exploration and exploitation in evolutionary algorithms: A survey
ACM Computing Surveys (CSUR)
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Terms such as exploitation, exploration, intensification and diversification are routinely employed in the metaheuristic literature to explain empirical runtime performance. Six prevalent views on exploitation and exploration are identified in the literature, each expressing a different aspect of these notions. The consistency and meaningfulness of these views are substantiated by their deducibility from the proposed novel definitions of exploitation and exploration, based on the hypothetical construct of a probable fitness landscape. This unifies, and thereby clarifies, the terminology and understanding of metaheuristics.