DARWIN: A Genetic Algorithm Language

Date

2013-10-29

Author

ARSLAN, Arslan
Üçoluk, Göktürk

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

162
views

0
downloads

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 with Genetic Algorithms, Programming Languages and Compilers.

Subject Keywords

Genetic algorithm, Parse tree, Abstract syntax tree, Genome representation, Genetic algorithm programming

URI

https://hdl.handle.net/11511/42472

DOI

https://doi.org/10.1007/978-3-319-01604-7_4

Collections

Department of Computer Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Gene reordering and concurrency in genetic algorithms Şehitoğlu, Onur Tolga; Üçoluk, Göktürk; Department of Computer Engineering (2002) 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...
Rigorous Analysis of Deformed Nanowires Using the Multilevel Fast Multipole Algorithm Karaosmanoglu, Bariscan; Yilmaz, Akif; Ergül, Özgür Salih (2015-05-17) We present accurate full-wave analysis of deformed nanowires using a rigorous simulation environment based on the multilevel fast multipole algorithm. Single nanowires as well as their arrays are deformed randomly in order to understand the effects of deformations to scattering characteristics of these structures. Results of hundreds of simulations are considered for statistically meaningful analysis of deformation effects. We show that deformations significantly enhance the forward-scattering abilities of ...
RRW: repeated random walks on genome-scale protein networks for local cluster discovery MACROPOL, Kathy; Can, Tolga; Singh, Ambuj K. (2009-09-09) Background: We propose an efficient and biologically sensitive algorithm based on repeated random walks (RRW) for discovering functional modules, e. g., complexes and pathways, within large-scale protein networks. Compared to existing cluster identification techniques, RRW implicitly makes use of network topology, edge weights, and long range interactions between proteins.
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...
Efficient and Accurate Electromagnetic Optimizations Based on Approximate Forms of the Multilevel Fast Multipole Algorithm Onol, Can; Karaosmanoglu, Bariscan; Ergül, Özgür Salih (2016-01-01) We present electromagnetic optimizations by heuristic algorithms supported by approximate forms of the multilevel fast multipole algorithm (MLFMA). Optimizations of complex structures, such as antennas, are performed by considering each trial as an electromagnetic problem that can be analyzed via MLFMA and its approximate forms. A dynamic accuracy control is utilized in order to increase the efficiency of optimizations. Specifically, in the proposed scheme, the accuracy is used as a parameter of the optimiz...

Citation Formats

A. ARSLAN and G. Üçoluk, “DARWIN: A Genetic Algorithm Language,” 2013, vol. 264, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/42472.