Fitness inheritance in genetic algorithms
SAC '95 Proceedings of the 1995 ACM symposium on Applied computing
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Self-Nonself Discrimination in a Computer
SP '94 Proceedings of the 1994 IEEE Symposium on Security and Privacy
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Multiobjective optimization using ideas from the clonal selection principle
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Immune inspired somatic contiguous hypermutation for function optimisation
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartI
Clonal selection with immune dominance and anergy based multiobjective optimization
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
MOBAIS: A Bayesian Artificial Immune System for Multi-Objective Optimization
ICARIS '08 Proceedings of the 7th international conference on Artificial Immune Systems
Engineering Applications of Artificial Intelligence
A diversity preserving selection in multiobjective evolutionary algorithms
Applied Intelligence
A hybrid multiobjective evolutionary algorithm: Striking a balance with local search
Mathematical and Computer Modelling: An International Journal
Mobile robot path planning using polyclonal-based artificial immune network
Journal of Control Science and Engineering - Special issue on Advances in Methods for Networked and Cyber-Physical System
| Hi-index | 0.00 |
The primary objective of this paper is to put forward a general framework under which clear definitions of immune operators and their roles are provided. To this aim, a novel Population Adaptive Based Immune Algorithm (PAIA) inspired by Clonal Selection and Immune Network theories for solving multi-objective optimization problems (MOP) is proposed. The algorithm is shown to be insensitive to the initial population size; the population and clone size are adaptive with respect to the search process and the problem at hand. It is argued that the algorithm can largely reduce the number of evaluation times and is more consistent with the vertebrate immune system than the previously proposed algorithms. Preliminary results suggest that the algorithm is a valuable alternative to already established evolutionary based optimization algorithms, such as NSGA II, SPEA and VIS.