Evolutionary Computing and Multi-Criteria Decision Making
For an introduction to our research, see:
- introduces multiobjective optimisation (MO) and trade-off surfaces
- explains how evolutionary algorithms can solve MO problems
- describes how decision makers can interact with our EMO algorithm and illustrates how design specifications are mapped into a MO formulation
- describes a typical design exercise
- illustrates how to visualise multiple (>3) ojectives
– explains the new challenges of solving MO problems containing more than 2 or 3 objectives
– describes some of the important research issues we are addressing
Recommended Further Reading
Fonseca CM and Fleming PJ: Genetic algorithms for multiobjective optimization: formulation, discussion and generalization, Proc Fifth International Conference on Genetic Algorithms, pp. 416 423, 1993.
Fonseca CM and Fleming PJ: Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part 1: A unified formulation, IEEE Transactions on Systems, Man and Cybernetics, vol. 28, no. 1, pp. 26-37, 1998.
Fonseca CM and Fleming PJ: Multiobjective optimization and multiple constraint handling with evolutionary algorithms - Part II: Application example, IEEE Transactions on Systems, Man and Cybernetics, Purshouse RC and Fleming PJ: Conflict, harmony, and independence: Relationships in evolutionary multi-criterion optimisation, Evolutionary Multi-Criterion Optimization, Proceedings, Lecture Notes In Computer Science, vol. 2632, pp16-30, 2003.
Fleming PJ, Purshouse RC, Lygoe RJ: Many-objective optimization: An engineering design perspective, Lecture Notes in Computer Science 3410, pp. 14-32, 2005.
Purshouse RC and Fleming PJ: On the evolutionary optimisation of many conflicting objectives, IEEE Transactions on Evolutionary Computation, 11(6), pp. 770-784, 2007.