This paper provides an article-length overview of the first half of a book manuscript that is forthcoming from Cambridge University Press. It is not intended to be a stand-alone piece; there are some loose ends in the paper that are addressed elsewhere in the larger manuscript. Nevertheless, the paper sketches out the core arguments from the first half of the book about party system transformat...

Aiming at the problem of complex path planning in processing curved surface workpieces body-in-white, a hybrid method based on memetic algorithm is proposed. The divided into two parts: welding sequence and between weld points. By establishing kinematic model spot robot pipper criterion z-y-z Euler angle solution method, motion constraints optimization are analyzed. Under framework algorithm, i...

This article introduces a scheme for clustering complex and linearly non-separable datasets, without any prior knowledge of the number of naturally occurring groups in the data. The proposed method is based on a modified version of classical Particle Swarm Optimization (PSO) algorithm, known as the Multi-elitist PSO (MEPSO) model. It also employs a kernel-induced similarity measure instead of t...

Recently, a general-purpose local-search heuristic method called Extremal Optimization (EO) has been successfully applied to some NP-hard combinatorial optimization problems. This paper presents an investigation on EO with its application in multiobjective optimization and proposes a new novel elitist (1+ λ ) multiobjective algorithm, called Multiobjective Extremal Optimization (MOEO). In order...

NSGA ( [5]) is a popular non-domination based genetic algorithm for multiobjective optimization. It is a very effective algorithm but has been generally criticized for its computational complexity, lack of elitism and for choosing the optimal parameter value for sharing parameter σshare. A modified version, NSGAII ( [3]) was developed, which has a better sorting algorithm , incorporates elitism...

A recently developed multimodal evolutionary algorithm [8] is used as engine for a learning classifier system. The evolutionary classifier is applied for two largely used data sets and the obtained results are compared with others of different algorithms. A noticeable gain over other computational techniques is that, besides the outcome for objects in the test set, the algorithm may also provid...

Purpose – The term selection problem for selecting query terms in information filtering and routing has been investigated using hill-climbers of various kinds, largely through the Okapi experiments in the TREC series of conferences. Although these are simple deterministic approaches which examine the effect of changing the weight of one term at a time, they have been shown to improve the retrie...

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