Although the interest in nature-inspired optimization of dynamic problems has been growing constantly over the past decade, very little has been done to analyze and characterize a changing fitness landscape. However, it would be very helpful for algorithm development to have a better understanding of the nature of fitness changes in dynamic real-world problems. In this paper, we propose a number of measures that can be used to analyze and characterize the dynamism in a problem changing over time. Additionally, we introduce a new dynamic multi-dimensional knapsack problem as a close-to-real-world test problem.

In this paper, an adaptive domination change mechanism for diploid genetic algorithms with discrete representations is presented. It is aimed at improving the performance of existing diploid genetic algorithms in changing environments. Diploidy acts as a source of diversity in the gene pool while the adaptive domination mechanism guides the phenotype towards an optimum. The combined effect of diploidy and the adaptive domination forms a balance between exploration and exploitation. The dominance characteristic of each locus in the population is adapted through feedback from the ongoing search process. A dynamic bit matching benchmark is used to perform controlled experiments. Controlled changes to implement different levels of change severities and frequencies are used. The testing phase consists of four stages. In the first stage, the benefits of the adaptive domination mechanism are shown by testing it against previously proposed diploid approaches. In the second stage, the same adaptive approach is applied to a haploid genetic algorithm to show the effect of the diploidy on the performance of the proposed approach. In the third stage, the levels of diversity introduced by diploidy on the genotype and maintained by the adaptive domination mechanism on the phenotype are explored. In the fourth stage, tests are performed to examine the robustness of the chosen approaches against different mutation rates. Currently, the dominance change mechanism can be applied to diallelic or multiallelic discrete representations and promising results are obtained as a result of the tests performed.

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Abstract.The effect of different representations has been thoroughly analyzed for evolutionary algorithms in stationary environments. However, the role of representations in dynamic environments has been largely neglected so far. In this paper, we empirically compare and analyze three different representations on the basis of a dynamic multi-dimensional knapsack problem. Our results indicate that indirect representations are particularly suitable for the dynamic multi-dimensional knapsack problem, because they implicitly provide a heuristic adaptation mechanism that improves the current solutions after a change.

If an optimisation algorithm performs a search in an environment that changes over time, it should be able to follow these changes and adapt itself for handling them in order to achieve good results. Different types of dynamics in a changing environment require the use of different approaches. Hyper-heuristics represent a class of methodologies that are high level heuristics performing search over a set of low level heuristics. Due to the generality of hyperheuristic frameworks, they are expected to be adaptive. Hence, a hyper-heuristic can be used in a dynamic environment to determine the approach to apply, adapting itself accordingly at each change. This study presents an initial investigation of hyper-heuristics in dynamic environments. A greedy hyper-heuristic is tested over a set of benchmark functions.