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. 1

This paper examines measures of diversity in genetic programming. The goal is to understand the importance of such measures and their relationship with fitness. Diversity methods and measures from the literature are surveyed and a selected set of measures are applied to common standard problem instances in an experimental study. Results show the varying definitions and behaviours of diversity and the varying correlation between diversity and fitness during different stages of the evolutionary process. Populations in the genetic programming algorithm are shown to become structurally similar while maintaining a high amount of behavioural differences. Conclusions describe what measures are likely to be important for understanding and improving the search process and why diversity might have different meaning for different problem domains.

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Abstract. In this paper we continue our study on adaptive genetic programming. We use Stepwise Adaptation of Weights (saw) to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems from literature. Also, we propose a model for randomly generating polynomials which we then use to further test all three gp variants. 1

In this paper we continue study on the Stepwise Adaptation of Weights (saw) technique. Previous studies on constraint satisfaction and data classification have indicated that saw is a promising technique to boost the performance of evolutionary algorithms. Here we use saw to boost performance of a genetic programming algorithm on simple symbolic regression problems. We measure the performance of a standard gp and two variants of saw extensions on two different symbolic regression problems.

Abstract. The fitness function of an evolutionary algorithm is one of the few possible spots where application knowledge can be made available to the algorithm. But the representation and use of knowledge in the fitness function is rather indirect and therefore not easy to achieve. In this paper, we present several case studies encoding application specific features into fitness functions for learning cooperative behavior of agents, an application that already requires complex and difficult to manipulate fitness functions. Our experiments with different variants of the Pursuit Game show that refining a knowledge feature already in the fitness function usually does not result in much difference in performance, while adding new application knowledge features to the fitness function improves the learning performance significantly. 1

Abstract- We study a selected group of hybrid EAs for solving CSPs, consisting of the best performing EAs from the literature. We investigate the contribution of the evolutionary component to their performance by comparing the hybrid EAs with their “de-evolutionarised ” variants. The experiments show that “de-evolutionarising ” can increase performance, in some cases doubling it. Considering that the problem domain and the algorithms are arbitrarily selected from the “memetic niche”, it seems likely that the same effect occurs for other problems and algorithms. Therefore, our conclusion is that after designing and building a memetic algorithm, one should perform a verification by comparing this algorithm with its “de-evolutionarised” variant. 1

Local search techniques have widespread use for solving propositional satisfiability problems. We investigate the use of adaptive local search techniques for model generation problems for modal logics; we focus on the modal logic S5. A local search algorithm extended with an adaptive heuristic is presented and tested on an ensemble of randomly generated problem instances. We briefly discuss the limitations of using local search for other NP-complete modal logics.

This paper This paper reports the results of experiments of a new technique which combines both of the ideas outlined above. The problem addressed is a sports rostering problem

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