Abstract
In interactive evolutionary computation (IEC), each solution is evaluated by a human user. Usually the total number of examined solutions is very small. In some applications such as hearing aid design and music composition, only a single solution can be evaluated at a time by a human user. Moreover, accurate and precise numerical evaluation is difficult. Based on these considerations, we formulated an IEC model with the minimum requirement for fitness evaluation ability of human users under the following assumptions: They can evaluate only a single solution at a time, they can memorize only a single previous solution they have just evaluated, their evaluation result on the current solution is whether it is better than the previous one or not, and the best solution among the evaluated ones should be identified after a pre-specified number of evaluations. In this paper, we first explain our IEC model in detail. Next we propose a (mu +1)ES-style algorithm for our IEC model. Then we propose an offline meta-level approach to automated algorithm design for our IEC model. The main feature of our approach is the use of a different mechanism (e.g., mutation, crossover, random initialization) to generate each solution to be evaluated. Through computational experiments on test problems, our approach is compared with the (mu +1)ES-style algorithm where a solution generation mechanism is pre-specified and fixed throughout the execution of the algorithm.
Highlights
Interactive evolutionary computation (IEC) is a class of evolutionary algorithms, which are based on subjective fitness evaluation by a human user (Takagi 2001)
In Ishibuchi et al (2012), we proposed the basic idea of our IEC model with the minimum requirement for human user’s fitness evaluation ability
We examine the effect of the following factors on the performance of automatically designed algorithms through computational experiments on a number of test problems: The number of runs used for evaluating each string Due to a stochastic nature of EC algorithms, usually a different solution is obtained from a different run of the same EC algorithm
Summary
Interactive evolutionary computation (IEC) is a class of evolutionary algorithms, which are based on subjective fitness evaluation by a human user (Takagi 2001). We examine the search ability of our (μ + 1)ES-style IEC algorithm for each combination of the four values of μ and the two settings for new solution generation mechanisms explained in the previous subsection (i.e., mutation only and crossover & mutation).
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