Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Niching methods for genetic algorithms
Niching methods for genetic algorithms
An introduction to software agents
Software agents
On agent-based software engineering
Artificial Intelligence
Introduction to Multiagent Systems
Introduction to Multiagent Systems
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
A new evolutionary model for detecting multiple optima
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Adaptive elitist-population based genetic algorithm for multimodal function optimization
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Evolving intelligent agents: A 50 year quest
IEEE Computational Intelligence Magazine
Computing with the social fabric: The evolution of social intelligence within a cultural framework
IEEE Computational Intelligence Magazine
Complex open-system design by quasi-agents: process-oriented modeling in agent-based systems
ACM SIGSOFT Software Engineering Notes
An evolutionary algorithm with species-specific explosion for multimodal optimization
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Effect of spatial locality on an evolutionary algorithm for multimodal optimization
EvoApplicatons'10 Proceedings of the 2010 international conference on Applications of Evolutionary Computation - Volume Part I
Evolutionary multimodal optimization using the principle of locality
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
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A novel approach to multimodal optimization called Roaming Agent-Based Collaborative Evolutionary Model (RACE) combining several evolutionary techniques with agent-based modeling is proposed. RACE model aims to detect multiple global and local optima by training a multi-agent system to employ various evolutionary techniques suitable for a specified multimodal optimization problem. Agents can exchange information during the search process enabling a cooperative search of optima between several populations evolving independently. Redundant search by multiple agents is avoided by having them communicate and negotiate about the space region searched. An agent can request and receive from another agent valuable information and genetic material for a better search of a certain region in the environment. Performance of the proposed agent-based collaborative evolutionary model is compared by means of numerical experiments with rival evolutionary techniques.