The research literature on metaheuristic and evolutionary computation has proposed a large number of algorithms for the solution of challenging real-world optimization problems. It is often not possible to study theoretically the performance of these algorithms unless significant assumptions are made on either the algorithm itself or the problems to which it is applied, or both. As a consequence, metaheuristics are typically evaluated empirically using a set of test problems. Unfortunately, relatively little attention has been given to the development of methodologies and tools for the large-scale empirical evaluation and/or comparison of metaheuristics. In this paper, we propose a landscape (test-problem) generator that can be used to generate optimization problem instances for continuous, bound-constrained optimization problems. The landscape generator is parameterized by a small number of parameters, and the values of these parameters have a direct and intuitive interpretation in terms of the geometric features of the landscapes that they produce. An experimental space is defined over algorithms and problems, via a tuple of parameters for any specified algorithm and problem class (here determined by the landscape generator). An experiment is then clearly specified as a point in this space, in a way that is analogous to other areas of experimental algorithmics, and more generally in experimental design. Experimental results are presented, demonstrating the use of the landscape generator. In particular, we analyze some simple, continuous estimation of distribution algorithms, and gain new insights into the behavior of these algorithms using the landscape generator.

A hybrid approach that combines the (1+1)-ES and threshold selection methods is developed. The framework of the new experimentalism is used to perform a detailed statistical analysis of the effects that are caused by this hybridization. Experimental results on the sphere function indicate that hybridization worsens the performance of the evolution strategy, because evolution strategies are well-scaled hillclimbers: the additional threshold disturbs the self-adaptation process of the evolution strategy. Theory predicts that the hybrid approach might be advantageous in the presence of noise. This e#ect could be observed--- however, a proper fine tuning of the algorithm's parameters appears to be advantageous.

The sequential parameter optimization (spot) package for R (R Development Core Team, 2008) is a toolbox for tuning and understanding simulation and optimization algorithms. Model-based investigations are common approaches in simulation and optimization. Sequential parameter optimization has been developed, because there is a strong need for sound statistical analysis of simulation and optimization algorithms. spot includes methods for tuning based on classical regression and analysis of variance techniques; tree-based models such as CART and random forest; Gaussian process models (Kriging) and combinations of different metamodeling approaches. This article exemplifies how spot can be used for automatic and interactive tuning. 1

Abstract- Sequential parameter optimization is a heuristic that combines classical and modern statistical techniques to improve the performance of search algorithms. To demonstrate its flexibility, three scenarios are discussed: (1) no experience how to choose the parameter setting of an algorithm is available, (2) a comparison with other algorithms is needed, and (3) an optimization algorithm has to be applied effectively and efficiently to a complex real-world optimization problem. Although sequential parameter optimization relies on enhanced statistical techniques such as design and analysis of computer experiments, it can be performed algorithmically and requires basically the specification of the relevant algorithm’s parameters. 1

Abstract — Particle swarm optimiser and genetic algorithm populations are macro-organisms, which perceive their environment as if filtered via a kernel. The kernel assimilates each individual’s sensory abilities so that the collective moves using a greedy hill-climbing strategy. This model is fitted to data collected in real PSO and GA runs by using genetic programming to evolve the kernel. In nature animals tend to live within groups. The social interactions effectively transform the fitness selection landscape seen by an isolated individual. In some cases a group behaves (or even can be said to think) like a single organism. Kernels provide a lens which coarse-grains or averages individual senses and so may help explain joint actions and social responses. The original multi-modal problem is smoothed by convolving it with a problem specific filter designed by GP. Because populations see the transformed social fitness landscape, they can pass over local optima. GP can give a good fit between the predicted behaviour of the macroscopic organism and the actual runs. I.

In recent years, there has been a growing interest in the development of experimental methodology in metaheuristics. In the field, researchers continue to develop and propose new algorithms at a rapid rate. While theoretical analysis has made progress and continues to develop, it is often the case that algorithms are evaluated and analyzed via empirical techniques. A vital component of