Sorting by insertion of leading elements
Journal of Combinatorial Theory Series A
Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Simulated annealing: theory and applications
Simulated annealing: theory and applications
Bandwidth packing: a tabu search approach
Management Science
Distances between traveling salesman tours
Discrete Applied Mathematics
GAS, a concept on modeling species in genetic algorithms
Artificial Intelligence
Sorting Permutations by Reversals and Eulerian Cycle Decompositions
SIAM Journal on Discrete Mathematics
Fitness landscapes and memetic algorithm design
New ideas in optimization
Tabu Search
A Bionomic Approach to the Capacitated p-Median Problem
Journal of Heuristics
Connections between cutting-pattern sequencing, VLSI Design, and flexible machines
Computers and Operations Research
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
A multiple-population evolutionary approach to gate matrix layout
International Journal of Systems Science
Local Search Genetic Algorithms for the Job Shop Scheduling Problem
Applied Intelligence
Linear gate assignment: a fast statistical mechanics approach
IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
A multiple-population evolutionary approach to gate matrix layout
International Journal of Systems Science
From adaptive to more dynamic control in evolutionary algorithms
EvoCOP'11 Proceedings of the 11th European conference on Evolutionary computation in combinatorial optimization
Pareto autonomous local search
LION'05 Proceedings of the 5th international conference on Learning and Intelligent Optimization
Intensification/Diversification-Driven ILS for a graph coloring problem
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
International Journal of Metaheuristics
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An implicit tenet of modern search heuristics is that there is a mutually exclusive balance between two desirable goals: search diversity (or distribution), i.e., search through a maximum number of distinct areas, and, search intensity, i.e., a maximum search exploitation within each specific area. We claim that the hypothesis that these goals are mutually exclusive is false in parallel systems. We argue that it is possible to devise methods that exhibit high search intensity and high search diversity during the whole algorithmic execution. It is considered how distance metrics, i.e., functions for measuring diversity (given by the minimum number of local search steps between two solutions) and coordination policies, i.e., mechanisms for directing and redirecting search processes based on the information acquired by the distance metrics, can be used together to integrate a framework for the development of advanced collective search methods that present such desiderata of search intensity and search diversity under simultaneous coexistence. The presented model also avoids the undesirable occurrence of a problem we refer to as the `ergometric bike phenomenon'. Finally, this work is one of the very few analysis accomplished on a level of meta-meta-heuristics, because all arguments are independent of specific problems handled (such as scheduling, planning, etc.), of specific solution methods (such as genetic algorithms, simulated annealing, tabu search, etc.) and of specific neighborhood or genetic operators (2-opt, crossover, etc.).