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
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
A species conserving genetic algorithm for multimodal function optimization
Evolutionary Computation
Multiobjective Optimization Using Evolutionary Algorithms - A Comparative Case Study
PPSN V Proceedings of the 5th International Conference on Parallel Problem Solving from Nature
A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Island Model Cooperating with Speciation for Multimodal Optimization
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Diversity-Guided Evolutionary Algorithms
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
An analysis of the behavior of a class of genetic adaptive systems.
An analysis of the behavior of a class of genetic adaptive systems.
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
Endocrine Control Evolutionary Algorithm
SYNASC '10 Proceedings of the 2010 12th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing
Improving PSO-Based multi-objective optimization using crowding, mutation and ∈-dominance
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
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This paper proposes an optimization technique inspired by the endocrine system, in particular by the intrinsic mechanism of hormonal regulation. The approach is applicable for many optimization problems, such as multimodal optimization in a static environment, multimodal optimization in a dynamic environment and multi-objective optimization. The advantage of this technique is that it is intuitive and there is no need for a supplementary mechanism to deal with dynamic environments, nor for major revisions in a multi-objective context. The Endocrine Control Evolutionary Algorithm (ECEA) is described. The ECEA is able to estimate and track the multiple optima in a dynamic environment. For multi-objective optimization problems, the issue of finding a good definition of optimality is solved naturally without using Pareto non-dominated in performance evaluation. Instead, the overall preference of the solution is used for fitness assignment. Without any adjustments, just by using a suitable fitness assignment, the ECEA algorithm performs well for the multi-objective optimization problems.