Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Theoretical Computer Science
Introduction to Reinforcement Learning
Introduction to Reinforcement Learning
Evolution strategies –A comprehensive introduction
Natural Computing: an international journal
Journal of Global Optimization
Genetic algorithms with multi-parent recombination
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Autonomy Oriented Computing: From Problem Solving to Complex Systems Modeling (Multiagent Systems, Artificial Societies, and Simulated Organizations)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
Differential Evolution: In Search of Solutions (Springer Optimization and Its Applications)
A hybrid genetic algorithm and bacterial foraging approach for global optimization
Information Sciences: an International Journal
Artificial Bee Colony (ABC) Optimization Algorithm for Solving Constrained Optimization Problems
IFSA '07 Proceedings of the 12th international Fuzzy Systems Association world congress on Foundations of Fuzzy Logic and Soft Computing
Design by Evolution: Advances in Evolutionary Design
Design by Evolution: Advances in Evolutionary Design
Dynamic Population-Based Particle Swarm Optimization Combined with Crossover Operator
HIS '09 Proceedings of the 2009 Ninth International Conference on Hybrid Intelligent Systems - Volume 01
Ant colony system: a cooperative learning approach to the traveling salesman problem
IEEE Transactions on Evolutionary Computation
The particle swarm - explosion, stability, and convergence in amultidimensional complex space
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
Ant system: optimization by a colony of cooperating agents
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A Multi-Facet Survey on Memetic Computation
IEEE Transactions on Evolutionary Computation
AI Communications - 18th RCRA International Workshop on “Experimental evaluation of algorithms for solving problems with combinatorial explosion”
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A novel optimization paradigm, called Community of Scientists Optimization (CoSO), is presented in this paper. The approach is inspired to the behaviour of a community of scientists interacting, pursuing for research results and foraging the funds needed to held their research activities. The CoSO metaphor can be applied to general optimization domains, where optimal solutions emerge from the collective behaviour of a distributed community of interacting autonomous entities. The CoSO framework presents analogies and remarkable differences with other evolutionary optimization approaches: swarm behaviour, foraging and selection mechanism based on research funds competition, dynamically evolving multicapacity communication channels realized by journals and evolving population size regulated by research management strategies. Experiments and comparisons on benchmark problems show the effectiveness of the approach for numerical optimization. CoSO, with the design of appropriate foraging and competition strategies, also represents a great potential as a general meta-heuristic for applications in non-numerical and agent-based domains.