Genetic Algorithms and the Immune System
PPSN I Proceedings of the 1st Workshop on Parallel Problem Solving from Nature
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Self-Adaptive Genetic Algorithms with Simulated Binary Crossover
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
Performance scaling of multi-objective evolutionary algorithms
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
Proceedings of the 6th international conference on Artificial immune systems
ICARIS'07 Proceedings of the 6th international conference on Artificial immune systems
Immune system modeling: the OO way
ICARIS'06 Proceedings of the 5th international conference on Artificial Immune Systems
Proceedings of the 4th international conference on Artificial Immune Systems
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
An orthogonal genetic algorithm for multimedia multicast routing
IEEE Transactions on Evolutionary Computation
An orthogonal genetic algorithm with quantization for globalnumerical optimization
IEEE Transactions on Evolutionary Computation
Intelligent evolutionary algorithms for large parameter optimization problems
IEEE Transactions on Evolutionary Computation
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In this study, we design an Orthogonal Immune Algorithm (OIA) for numerical optimization by incorporating orthogonal initialization, a novel neighborhood orthogonal cloning operator, a static hypermutation operator, and a novel diversity-based selection operator. The OIA is unique in three respects: Firstly, a new selection method based on orthogonal arrays is provided in order to maintain diversity in the population. Secondly, the orthogonal design with quantization technique is introduced to generate initial population. Thirdly, the orthogonal design with the modified quantization technique is introduced into the cloning operator. In order to identify any improvement due to orthogonal initialization, diversity-based selection and neighborhood orthogonal cloning, we modify the OIA via replacing its orthogonal initialization by random initialization; replacing its diversity-based selection by a standard evolutionary operator (1/4+»)-selection operator; and replacing its neighborhood orthogonal cloning by proportional cloning, and compare the four version algorithms in solving eight benchmark functions and six composition functions.