Genetic algorithms (GA) are stimulated by population genetics and evolution at the
population level where crossover and mutation comes from random variables. The problems …

The timetabling of lecturers, seminars, practical sessions and examinations is a core
business process for academic institutions. A feasible timetable must satisfy hard …

In this paper, we propose a novel dynamic discrete framework to address image morphing
with application to optical flow estimation. We reformulate the problem using a number of …

The layout of manufacturing facilities has a large impact on manufacturing performance. The
layout design process produces a block plan that shows the relative positioning of resources …

Metaheuristics are approximation methods used to solve combinatorial optimization
problems. Their performance usually depends on a set of parameters that need to be …

Since their first formulation, genetic algorithms (GAs) have been one of the most widely used
techniques to solve combinatorial optimization problems. The basic structure of the GAs is …

This study aims to analyze the effect of genetic algorithm parameters on the distribution
mileage of the largest logistics service provider in Central Jakarta using a general full …

Gezgin satıcı problemi, optimizasyon alanında araştırmacı ve akademisyenler tarafından
üzerinde uzun yıllardır yoğun olarak çalışılan çözümü zor (NP-hard) bir problemdir. Genetik …

Production scheduling in multiple-stage multiple-machine multiple-product environment is a
NP hard problem usually faced by make/engineer-to-order companies engaged in capital …

Metaheuristics employ a variety of different components using a wide array of operators to
execute their search. This determines their intensification, diversification and all other …