Gene reordering and concurrency in genetic algorithms

Şehitoğlu, Onur Tolga
This study first introduces an order-free chromosome encoding to enhance the performance of genetic algorithms by learning the linkage of building blocks in non-binary encodings. The method introduces a measure called affinity which is based on the statistical properties of gene valuations in the population. It uses the affinity values of the local and global gene pairs to construct a global permutation with tight building block positioning. Method is tested and experimental results are shown for a group of deceptive and real life test problems. Then, study proposes a gene level concurrency model where each gene position is implemented on a different process. This combines the advantages of implicit parallelism and a chromosome structure free approach. It also helps implementation of gene reordering method introduced and probably other non-linear chromosome encodings.


Gene Level Concurrency in Genetic Algorithms
Şehitoğlu, Onur Tolga; Üçoluk, Göktürk (Springer-Verlag, 2007-01-01)
This study describes an alternative concurrency approach in genetic algorithms. Inspiring from implicit parallelism in a physical chromosome, a vertical concurrency is introduced. Proposed gene process model allows genetic algorithms work in encodings independent from the gene position ordering in a chromosome. This feature is used to implement a gene reordering version of genetic algorithm. Further possible models of flexible gene position encodings are discussed.
Robust multiobjective evolutionary feature subset selection algorithm for binary classification using machine learning techniques
Deniz, Ayca; Kiziloz, Hakan Ezgi; Dokeroglu, Tansel; Coşar, Ahmet (2017-06-07)
This study investigates the success of a multiobjective genetic algorithm (GA) combined with state-of-the-art machine learning (ML) techniques for the feature subset selection (FSS) in binary classification problem (BCP). Recent studies have focused on improving the accuracy of BCP by including all of the features, neglecting to determine the best performing subset of features. However, for some problems, the number of features may reach thousands, which will cause too much computation power to be consumed ...
Comparison of Facial Alignment Techniques: With Test Results on Gender Classification Task
Kaya, Tunç Güven (2014-08-24)
In this paper, different facial alignment techniques are revised in terms of their effects on machine learning algorithms. This paper, investigates techniques that are widely accepted in literature and measures their effect on gender classification task. There is no special reason on selecting gender classification task, any other task could have been chosen. In audience measurement systems, many important demographics, i.e. gender, age, facial expression, can be measured by using machine learning algorithm...
DARWIN: A Genetic Algorithm Language
ARSLAN, Arslan; Üçoluk, Göktürk (2013-10-29)
This article describes the DARWIN Project, which is a Genetic Algorithm programming language and its C Cross-Compiler. The primary aim of this project is to facilitate experimentation of Genetic Algorithm solution representations, operators and parameters by requiring just a minimal set of definitions and automatically generating most of the program code. The syntax of the DARWIN language and an implementational overview of the the cross-compiler will be presented. It is assumed that the reader is familiar ...
Development of a grid-aware master worker framework for artificial evolution
Ketenci, Ahmet; Şener, Cevat; Department of Computer Engineering (2010)
Genetic Algorithm (GA) has become a very popular tool for various kinds of problems, including optimization problems with wider search spaces. Grid search techniques are usually not feasible or ineffective at finding a solution, which is good enough. The most computationally intensive component of GA is the calculation of the goodness (fitness) of candidate solutions. However, since the fitness calculation of each individual does not depend each other, this process can be parallelized easily. The easiest wa...
Citation Formats
O. T. Şehitoğlu, “Gene reordering and concurrency in genetic algorithms,” Ph.D. - Doctoral Program, Middle East Technical University, 2002.