A two-level evolution strategy : balancing global and local search

Evolution Strategies apply mutation and recombination operators in order to create their offspring. Both operators have a different role in the evolution process: recombination should combine information of different individuals, while mutation performs a kind of random walk to introduce new values. In an ES these operators are always applied together, but their different roles suggest that it might be better to apply them independently and at different rates. In order to do so the ES has been split into two levels. The resulting Modular Evolution Strategy consists of a population of local optimizers and a distributed population manager. Both parts have their own specific role in the optimization process. As a result of its modularity this method can be adapted more easily to specific classes of numerical optimization problems, and introduction of adaptive mechanisms is relatively easy. A further interesting aspect about this algorithm is that it does not need any global communication, and therefore can be parallelized easily. Many problems can be expressed as numerical optimization problems. Especially when the dimension of the input space and the number of local optima is high these problems tend to be very difficult. In order to obtain an efficient solver one has to gather information regarding the function to be optimized. Evolution based learning can be used to obtain this information. This paper contains results obtained with the Modular Evolution Strategy and compares these results to those obtained with other evolution based method. The results look promising

Building block filtering and mixing

A three-stage evolutionary method, the BBF-GA is introduced. BBF-GA is an acronym for building block filtering genetic algorithm. During the first stage, an ensemble of fast evolutionary algorithms is used to explore the search space. The best individual found by each of these evolutionary algorithms is propagated to the next phase. During the second stage, building block filtering is used to extract the essential parts of each of these local optimal strings, and masks these essential parts. During the third stage, a single evolutionary algorithm is used to find the global optimum by recombining the masked strings. For this purpose we use a special recombination operator that exploits the information stored in the masks. Given an appropriate basis, such that partial solutions can be discovered and evaluated in parallel and be combined afterwards, a recombination-based evolutionary algorithm can be very efficient. Therefore, learning of the structure of problem-spaces is important to make a more efficient recombination possible. The BBF-GA is a first step along this line for binary search spaces and problems that adhere to the building block hypothesis

Comparison of selection schemes for evolutionary constrained optimization

Evolutionary algorithms simulate the process of evolution in order to evolve solutions to optimization problems. An interesting domain of application is to solve numerical constrained optimization problems. We introduce a simple constrained optimization problem with scalable dimension, adjustable complexity, and a known optimal solution. A set of evolutionary algorithms, all using different selection schemes, is applied to this problem. The performance of the evolutionary algorithms differs strongly. Selection schemes that only use a limited number of offspring as parents for the next generation consistently outperform the schemes that accept all offspring as parents and adjust their fertility based on (relative) fitness during the experiments

Explicit filtering of building blocks for genetic algorithms

Genetic algorithms are often applied to building block problems. We have developed a simple filtering algorithm that can locate building blocks within a bit-string, and does not make assumptions regarding the linkage of the bits. A comparison between the filtering algorithm and genetic algorithms reveals some interesting insights, and we discuss how the filtering algorithm can be used to build a powerful hybrid genetic algorithm

An evolutionary approach to time constrained routing problems

Routing problems are an important class of planning problems. Usually there are many different constraints and optimization criteria involved, and it is difficult to find general methods for solving routing problems. We propose an evolutionary solver for such planning problems. An instance of this solver has been tested on a specific routing problem with time constraints. The performance of this evolutionary solver is compared to a biased random solver and a biased hillclimber solver. Results show that the evolutionary solver performs significantly better than the other two solvers

Orgy in the computer: multi-parent reproduction in genetic algorithms

In this paper we investigate the phenomenon of multi-parent reproduction, i.e. we study recombination mechanisms where an arbitrary $n>1$ number of parents participate in creating children. In particular, we discuss scanning crossover that generalizes the standard uniform crossover and diagonal crossover that generalizes 1-point crossover, and study the effects of different number of parents on the GA behavior. We conduct experiments on tough function optimization problems and observe that by multi-parent operators the performance of GAs can be enhanced significantly. We also give a theoretical foundation by showing how these operators work on distributions

The influence of evolutionary selection schemes on the iterated prisoner's dilemma

Many economic and social systems are essentially large multi-agent systems. By means of computational modeling, the complicated behavior of such systems can be investigated. Modeling a multi-agent system as an evolutionary agent system, several important choices have to be made for evolutionary operators. Especially, it is to be expected that evolutionary dynamics substantially depend on the selection scheme. We therefore investigate the influence of evolutionary selection mechanisms on a fundamental problem: the iterated prisoner's dilemma (IPD), an elegant model for the emergence of cooperation in a multi-agent system. We observe various types of behavior, cooperation level, and stability, depending on the selection mechanism and the selection intensity. Hence, our results are important for (1) The proper choice and application of election schemes when modeling real economic situations and (2) assessing the validity of the conclusions drawn from computer experiments with these models. We also conclude that the role of selection in the evolution of multi-agent systems should be investigated further, for instance using more detailed and complex agent interaction models

Density-based unsupervised classification for remote sensing

Most image classification methods are supervised and use a parametric model of the classes that have to be detected. The models of the different classes are trained by means of a set of training regions that usually have to be marked and classified by a human interpreter. Unsupervised classification methods are data-driven methods that do not use such a set of training samples. Instead, these methods look for (repeated) structures in the data. In this paper we describe a non-parametric unsupervised classification method. The method uses biased sampling to obtain a learning sample with little noise. Next, density estimation based clustering is used to find the structure in the learning data. The method generates a non-parametric model for each of the classes and uses these models to classify the pixels in the image

Evolutionary air traffic flow management for large 3D-problems

We present an evolutionary tool to solve free-route Air Traffic Flow Management problems within a three-dimensional air space. This is the first evolutionary tool which solves free-route planning problems involving a few hundred aircraft. We observe that the importance of the recombination operator increases as we scale to larger problem instances. The evolutionary algorithm is based on a variant of the elitist recombinationalgorithm. We show a theoretical analysis of the problem, and present the results of experiments