Search · four archives
Search · four archives
7 papers · ranked by Valyu relevance
Frank Neumann, Dirk Sudholt, Carsten Witt
The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the CLIFF function for which it has been shown recently that…
Prasanta Dutta, Anirban Mukhopadhyay
—Compact Genetic Algorithms (cGAs) are condensed variants of classical Genetic Algorithms (GAs) that use a probability vector representation of the population instead of the complete population. cGAs have been shown to significantly reduce the number of function evaluations required while producing outcomes similar to…
Marcel Chwiałkowski, Benjamin Doerr, Martin S. Krejca
—The compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA) another simple EDA—, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to…
Sevil Zanjani Miyandoab, Shahryar Rahnamayan, Azam Asilian Bidgoli
— Feature selection is an expensive challenging task in machine learning and data mining aimed at removing irrelevant and redundant features. This contributes to an improvement in classification accuracy, as well as the budget and memory requirements for classification, or any other postprocessing task conducted after…
W. B. Langdon
We summarise how a 3.0GHz 16 core AVX512 computer can interpret the equivalent of up to on average 1 103 370 000 000 GPop/s. Citations to existing publications are given. Implementation stress is placed on both parallel computing, bandwidth limits and avoiding repeated calculation. Information theory suggests in…
Sumit Adak, Carsten Witt
Generalized OneMax Authors: ['Sumit Adak' 'Carsten Witt'] Abstract. A class of metaheuristic techniques called estimation-of-distribution algorithms (EDAs) are employed in optimization as more sophisticated substitutes for traditional strategies like evolutionary algorithms. EDAs generally drive the search for the…
Y. Ma, Tan Chye Cheah
problem Authors: ['Y. Ma' 'Tan Chye Cheah'] This paper implements a new way of solving a problem called the traveling salesman problem (TSP) using quantum genetic algorithm (QGA). We compared how well this new approach works to the traditional method known as a classical genetic algorithm (CGA). The TSP is a…