Parameter Control in Evolutionary Algorithms

Abstract

Summary. The issue of setting the values of various parameters of an evolutionary algorithm is crucial for good performance. In this paper we discuss how to do this, beginning with the issue of whether these values are best set in advance or are best changed during evolution. We provide a classification of different approaches based on a number of complementary features, and pay special attention to setting parameters on-the-fly. This has the potential of adjusting the algorithm to the problem while solving the problem. This paper is intended to present a survey rather than a set of prescriptive details for implementing an EA for a particular type of problem. For this reason we have chosen to interleave a number of examples throughout the text. Thus we hope to both clarify the points we wish to raise as we present them, and also to give the reader a feel for some of the many possibilities available for controlling different parameters.

A harmony search algorithm for nurse rostering problems

“…There are, however, a group of problems represented with a common XML structure but associated with different constraints and objectives [26]. Some effort has also been made in the literature to construct relatively general frameworks [31,32]. Therefore, in this work we have attempted to solve real world and benchmark NRPs because we believe that HSA can be applied to solve a wide range of NRPs.…”

“…In the literature, shift patterns have been used to effectively construct rosters in highly constrained nurse scheduling problems [32,38,43]. Table 5 The possible one-week valid patterns As for the benchmark instances, the set of initial solutions in HM is created by using a neighborhood operator which incrementally adds new shifts to the roster until all nurses have been scheduled [31] [32].…”

“…In addition, the parameter values of HSA in [30] were arbitrary chosen. It is well known that the performance of many population based methods is highly dependent on its parameter values [31]. Therefore, the aim of this work is to address research issues in applying HSA to NRPs by intensive investigations on suitable parameter values and performance evaluation on a large number of instances of problems with different size and complexity.…”

Analyzing bandit-based adaptive operator selection mechanisms

“…After [11,12], parameter setting in EAs proceeds along two main modes. Off-line or external tuning, referred to as parameter tuning, determines a priori the appropriate parameter values.…”

“…The contemporary view of EAs, however, acknowledges that specific problems require specific setups for satisfactory performance [12]: when it comes to solving a given problem, parameter setting is viewed as the Achilles' heel of EAs, on par with their high computational cost.…”