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Evolutionary computation has become an important problem solving methodology among many researchers. The population-based collective learning process, selfadaptation, and robustness are some of the key features of evolutionary algorithms when compared to other global optimization techniques. Even though evolutionary computation has been widely accepted for solving several important practical applications...
Since they were proposed as an optimization method, evolutionary algorithms (EA) have been used to solve problems in several research fields. This success is due, besides other things, to the fact that these algorithms do not require previous information regarding the problem to be optimized and offer a high degree of parallelism. However, some problems are computationally intensive regarding the...
This chapter deals with the application of hybrid evolutionary methods to design optimization issues in which approximation techniques and model management strategies can be used to guide the decision making process in a multidisciplinary context. An enhanced evolutionary algorithmic scheme devoted to design optimization is proposed, and its use in real applications is illustrated in the framework...
A challenge in hybrid evolutionary algorithms is to employ efficient strategies to cover all the search space, applying local search only in actually promising search areas. The inspiration in nature has been pursued to design flexible, coherent, and efficient computational models. In this chapter, the clustering search (*CS) is proposed as a generic way of combining search metaheuristics with clustering...
This chapter presents a new algorithm for function optimization problems, particle swarm assisted incremental evolution strategy (PIES), which is designed for enhancing the performance of evolutionary computation techniques by evolving the input variables incrementally. The whole evolution consists of several phases and one more variable is focused in each phase. The number of phases is equal to the...
In order to build an efficient nearest neighbor classifier three objectives have to be reached: achieve a high accuracy rate, minimize the set of prototypes to make the classifier tractable even with large databases, and finally, reduce the set of features used to describe the prototypes. Irrelevant or redundant features are likely to contribute more noise than useful information. These objectives...
This chapter proposes a hybrid approach by combining a Euclidian distance (EU) based genetic algorithm (GA) and particle swarm optimization (PSO) method. The performance of the hybrid algorithm is illustrated using four test functions. Proportional integral derivative (PID) controllers have been widely used in industrial systems such as chemical process, biomedical process, and in the main steam temperature...
The social foraging behavior of Escherichia coli (E. Coli) bacteria has been used to solve optimization problems. This chapter proposes a hybrid approach involving genetic algorithm (GA) and bacterial foraging (BF) algorithm for function optimization problems. We first illustrate the proposed method using four test functions and the performance of the algorithm is studied with an emphasis on mutation,...
Numerous physical models have been developed in order to describe the physical and chemical processes constituting the optical microlithography process. Many of these models depend on parameters that have to be calibrated against experimental data. An optimization routine using a genetic algorithm (GA) proved a feasible approach in order to find adequate model parameters. However, the high computation...
Protein folding prediction (PFP), especially the ab initio approach, is one of the most challenging problems facing the bioinformatics research community due to it being extremely complex to solve and computationally very intensive. Hybrid evolutionary computing techniques have assumed considerable importance in attempting to overcome these challenges and so this chapter explores some of...
In this chapter we present a hybrid evolutionary metaheuristic based on memetic algorithms (MAs) and several local search algorithms. The memetic algorithm is used as the principal heuristic that guides the search and can use any of the 16 local search algorithms during the search process. The local search algorithms used in combination with the MA are obtained by fixing either the type of the neighborhood...
Clustering genes based on their expression profiles is usually the first step in geneexpression data analysis. Among the many algorithms that can be applied to gene clustering, the k-means algorithm is one of the most popular techniques. This is mainly due to its ease of comprehension, implementation, and interpretation of the results. However, k-means suffers from some problems, such as the need...
We present a hybrid method to perform parametric image registration. The objective is to find the best set of parameters to match a transformed image (possible with noise) to a target image. Hybridization occurs when genetic algorithms are able to determine rough areas of the parameter optimization space, but fail to produce fine tunings for those parameters. In that case, the Newton–Levenberg–Marquardt...
Traveling Salesperson Problem (TSP) is among the most-studied and hard combinatorial optimization problems and has been investigated by numerous researchers of both theoretical and practical interests. Biobjective version of the problem is harder because it requires obtaining a set of diverse and equivalent (nondominated) solutions forming a Paretofront, rather a single solution. Multiobjective Evolutionary...
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