Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
A solvable model of a hard optimisation problem
Theoretical aspects of evolutionary computing
New Genetic Local Search Operators for the Traveling Salesman Problem
PPSN IV Proceedings of the 4th International Conference on Parallel Problem Solving from Nature
On the Analysis of Evolutionary Algorithms - A Proof That Crossover Really Can Help
ESA '99 Proceedings of the 7th Annual European Symposium on Algorithms
When a genetic algorithm outperforms hill-climbing
Theoretical Computer Science
Completely Derandomized Self-Adaptation in Evolution Strategies
Evolutionary Computation
A building-block royal road where crossover is provably essential
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Optimal parameters for search using a barrier tree Markov model
Theoretical Computer Science
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Theoretical Computer Science
Backbones and backdoors in satisfiability
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Backdoors to typical case complexity
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Real royal road functions-where crossover provably is essential
Discrete Applied Mathematics - Special issue: Boolean and pseudo-boolean funtions
Learning the large-scale structure of the MAX-SAT landscape using populations
IEEE Transactions on Evolutionary Computation
No free lunch theorems for optimization
IEEE Transactions on Evolutionary Computation
Modeling crossover-induced linkage in genetic algorithms
IEEE Transactions on Evolutionary Computation
The analysis of a recombinative hill-climber on H-IFF
IEEE Transactions on Evolutionary Computation
Symmetry breaking in population-based optimization
IEEE Transactions on Evolutionary Computation
Large Barrier Trees for Studying Search
IEEE Transactions on Evolutionary Computation
Towards a deeper understanding of trade-offs using multi-objective evolutionary algorithms
EvoApplications'12 Proceedings of the 2012t European conference on Applications of Evolutionary Computation
Information Sciences: an International Journal
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Applied Soft Computing
Compact Particle Swarm Optimization
Information Sciences: an International Journal
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This paper identifies five distinct mechanisms by which a population-based algorithm might have an advantage over a solo-search algorithm in classical optimization. These mechanisms are illustrated through a number of toy problems. Simulations are presented comparing different search algorithms on these problems. The plausibility of these mechanisms occurring in classical optimization problems is discussed. The first mechanism we consider relies on putting together building blocks from different solutions. This is extended to include problems containing critical variables. The second mechanism is the result of focusing of the search caused by crossover. Also discussed in this context is strong focusing produced by averaging many solutions. The next mechanism to be examined is the ability of a population to act as a low-pass filter of the landscape, ignoring local distractions. The fourth mechanism is a population's ability to search different parts of the fitness landscape, thus hedging against bad luck in the initial position or the decisions it makes. The final mechanism is the opportunity of learning useful parameter values to balance exploration against exploitation.