In recent years dynamic optimization problems have attracted a growing interest from the community of genetic algorithms with several approaches developed to address these problems...
In this paper we study a number of issues related to the design of a cellular genetic algorithm (cGA) for multiobjective optimization. We take as an starting point an algorithm fol...
This paper presents a comprehensive comparison between the performance of state-of-the-art genetic algorithms NSGA-II, SPEA2 and IBEA and their differential evolution based variant...
A novel evolutionary approach for the bin packing problem (BPP) is presented. A simple steady-state genetic algorithm is developed that produces results comparable to other approa...
The optimal sizing of a small autonomous hybrid power system can be a very challenging task, due to the large number of design settings and the uncertainty in key parameters. This ...
Yiannis A. Katsigiannis, Pavlos S. Georgilakis, Em...
Genetic algorithms are a robust adaptive optimization technique based on a biological paradigm. They perform efficient search on poorly-defined spaces by maintaining an ordered po...
Selecting a set of features which is optimal for a given task is a problem which plays an important role in a wide variety of contexts including pattern recognition, adaptive cont...
In this paper, we show how genetic algorithms can be used to solve en-route aircraft conflict automatically to increase Air Traffic Control capacity in high density areas. The ATC
The field of Genetic Algorithms has grown into a huge area over the last few years. Genetic Algorithms are adaptive methods, which can be used to solve search and optimisation pro...
Abstract--Recombination operator plays a very important role in genetic algorithms. In this paper, we present binary coded genetic algorithms in which more than two parents are inv...