TY - GEN
T1 - Overview of artificial immune systems for multi-objective optimization
AU - Campelo, Felipe
AU - Guimarães, Frederico G.
AU - Igarashi, Hajime
PY - 2007/3/31
Y1 - 2007/3/31
N2 - Evolutionary algorithms have become a very popular approach for multiobjective optimization in many fields of engineering. Due to the outstanding performance of such techniques, new approaches are constantly been developed and tested to improve convergence, tackle new problems, and reduce computational cost. Recently, a new class of algorithms, based on ideas from the immune system, have begun to emerge as problem solvers in the evolutionary multiobjective optimization field. Although all these immune algorithms present unique, individual characteristics, there are some trends and common characteristics that, if explored, can lead to a better understanding of the mechanisms governing the behavior of these techniques. In this paper we propose a common framework for the description and analysis of multiobjective immune algorithms.
AB - Evolutionary algorithms have become a very popular approach for multiobjective optimization in many fields of engineering. Due to the outstanding performance of such techniques, new approaches are constantly been developed and tested to improve convergence, tackle new problems, and reduce computational cost. Recently, a new class of algorithms, based on ideas from the immune system, have begun to emerge as problem solvers in the evolutionary multiobjective optimization field. Although all these immune algorithms present unique, individual characteristics, there are some trends and common characteristics that, if explored, can lead to a better understanding of the mechanisms governing the behavior of these techniques. In this paper we propose a common framework for the description and analysis of multiobjective immune algorithms.
UR - https://www.scopus.com/pages/publications/37249000408
U2 - 10.1007/978-3-540-70928-2_69
DO - 10.1007/978-3-540-70928-2_69
M3 - Conference Contribution (Conference Proceeding)
AN - SCOPUS:37249000408
SN - 9783540709275
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 937
EP - 951
BT - Evolutionary Multi-Criterion Optimization - 4th International Conference, EMO 2007, Proceedings
PB - Springer Verlag
T2 - 4th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2007
Y2 - 5 March 2007 through 8 March 2007
ER -