Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
A Variant of Evolution Strategies for Vector Optimization
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Some experiments in machine learning using vector evaluated genetic algorithms (artificial intelligence, optimization, adaptation, pattern recognition)
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Multi-objective genetic algorithms: Problem difficulties and construction of test problems
Evolutionary Computation
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
WBMOAIS: A novel artificial immune system for multiobjective optimization
Computers and Operations Research
Advances in artificial immune systems
IEEE Computational Intelligence Magazine
ICARIS'11 Proceedings of the 10th international conference on Artificial immune systems
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Multiobjective optimization problems have been solved in recent years by several researchers using different kind of algorithms, among them genetic and evolutionary algorithms and artificial immune systems. The results obtained during these tests were satisfactory, but these researchers observed that there still is a need for new ideas for algorithms which will increase efficiency and at the same time decrease the computational effort. In this paper the idea of coupling of immune algorithms with game theory is presented. The authors take out the most important elements from the artificial immune system, such as clonal selection and suppression, and couple them with the idea of Nash equilibrium. The new approach and some preliminary tests and results are presented here.