Adaptation in natural and artificial systems
Adaptation in natural and artificial systems
Evolutionary computation: toward a new philosophy of machine intelligence
Evolutionary computation: toward a new philosophy of machine intelligence
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.)
Autonomous agents and multi-agent systems: explorations in learning, self-organization and adaptive computation
Multi-agent oriented constraint satisfaction
Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Multi-Agent Systems: An Introduction to Distributed Artificial Intelligence
Evolutionary Computation: The Fossil Record
Evolutionary Computation: The Fossil Record
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Proceedings of the 5th International Conference on Genetic Algorithms
Modeling Building-Block Interdependency
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Cellular Genetic Algorithms
An evolutionary autonomous agents approach to image featureextraction
IEEE Transactions on Evolutionary Computation
Parallel hybrid method for SAT that couples genetic algorithms andlocal search
IEEE Transactions on Evolutionary Computation
An organizational coevolutionary algorithm for classification
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Evolutionary Computation
A multiagent genetic algorithm for global numerical optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
A multiagent evolutionary algorithm for constraint satisfaction problems
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An Organizational Evolutionary Algorithm for Numerical Optimization
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
An anticentroid-oriented particle swarm algorithm for numerical optimization
AICI'10 Proceedings of the 2010 international conference on Artificial intelligence and computational intelligence: Part II
An improved co-evolution genetic algorithm for combinatorial optimization problems
ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part I
Global numerical optimization based on small-world networks
ICNC'06 Proceedings of the Second international conference on Advances in Natural Computation - Volume Part II
Constrained layout optimization in satellite cabin using a multiagent genetic algorithm
SEAL'12 Proceedings of the 9th international conference on Simulated Evolution and Learning
A self-organization method for reorganizing resources in a distributed network
AMT'12 Proceedings of the 8th international conference on Active Media Technology
A multi-agent genetic algorithm for resource constrained project scheduling problems
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Adaptation of a multiagent evolutionary algorithm to NK landscapes
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
Semantic to intelligent web era: building blocks, applications, and current trends
Proceedings of the Fifth International Conference on Management of Emergent Digital EcoSystems
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Based on our previous works, multiagent systems and evolutionary algorithms (EAs) are integrated to form a new algorithm for combinatorial optimization problems (CmOPs), namely, MultiAgent EA for CmOPs (MAEA-CmOPs). In MAEA-CmOPs, all agents live in a latticelike environment, with each agent fixed on a lattice point. To increase energies, all agents compete with their neighbors, and they can also increase their own energies by making use of domain knowledge. Theoretical analyses show that MAEA-CmOPs converge to global optimum solutions. Since deceptive problems are the most difficult CmOPs for EAs, in the experiments, various deceptive problems with strong linkage, weak linkage, and overlapping linkage, and more difficult ones, namely, hierarchical problems with treelike structures, are used to validate the performance of MAEA-CmOPs. The results show that MAEA-CmOP outperforms the other algorithms and has a fast convergence rate. MAEA-CmOP is also used to solve large-scale deceptive and hierarchical problems with thousands of dimensions, and the experimental results show that MAEA-CmOP obtains a good performance and has a low computational cost, which the time complexity increases in a polynomial basis with the problem size.