Constraint handling in evolutionary algorithms

Optimisation problems are abundant in all areas of science and engineering. Solving an optimisation problem amounts to choosing values for a set of decision variables that result in the best solution possible. Often, optimisation problems are constrained in that there are restrictions on the values that the decision variables can take on.*Evolutionary algorithms (EAs) are optimisation strategies that see increasing use in many areas of application. They iteratively improve the quality of populations of candidate solutions by subjecting them to variation and selection. Their robustness in the face of non-differentiable or noisy objectives, along with the relative ease with which they can be adapted to poorly understood problems, often make EAs the method of choice where other approaches are not applicable or prone to failure.*A multitude of techniques for handling constraints in EAs have been proposed and are in common use. However, knowledge with regard to their respective capabilities and shortcomings is limited. Most crucially, the interaction between adaptive variation operators and constraint handling techniques is poorly understood. I will achieve an understanding of scaling properties of EAs for constrained optimisation by analysing their behaviour for sets of carefully selected test problems. The results obtained will complement, extend, and help explain the large body of empirical knowledge generated on large function testbeds that is available today. I will then use the insights gained to develop more capable EAs for constrained optimisation and systematically compare their capabilities with those of other direct search strategies.*