Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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
Solving Multiobjective Optimization Problems Using an Artificial Immune System
Genetic Programming and Evolvable Machines
Comparison of Multiobjective Evolutionary Algorithms: Empirical Results
Evolutionary Computation
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Biologically Inspired Algorithms for Financial Modelling (Natural Computing Series)
Artificial immune systems---today and tomorrow
Natural Computing: an international journal
Multiobjective immune algorithm with nondominated neighbor-based selection
Evolutionary Computation
The measure of Pareto optima applications to multi-objective metaheuristics
EMO'03 Proceedings of the 2nd international conference on Evolutionary multi-criterion optimization
EC'05 Proceedings of the 3rd European conference on Applications of Evolutionary Computing
Introducing dendritic cells as a novel immune-inspired algorithm for anomaly detection
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Multiobjective optimization by a modified artificial immune system algorithm
ICARIS'05 Proceedings of the 4th international conference on Artificial Immune Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
The balance between proximity and diversity in multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
| Hi-index | 0.00 |
In everyday life, there are plentiful cases that we need to find good solutions such that risk, cost and many other factors are to be optimized. These problems are typical examples of multi-objective optimization problems. Evolutionary algorithms are often employed for solving it. Due to the characteristics of learning and adaptability, self-organization and memory capabilities, one of the biological inspired AI methods --- artificial immune systems (AIS) is considered to be a class of evolutionary techniques that can be deployed for solving this problem. This paper aims to propose a new AIS-based framework focusing on distributed and self-organization characteristics. Population of solutions is decomposed into sub-populations forming clusters. Sub-populations in each cluster undergo independent evolution processes. These clusters are then combined and re-decomposed. The proposed mechanism aims to reduce the complexity in the evolution processes, enhance the exploitation ability and achieve quick convergence. It is evaluated and compared with representative algorithms.