PES: A system for parallelized fitness evaluation of evolutionary methods

Date

2003-01-01

Author

Soysal, O
Bahceci, E
Şahin, Erol

Metadata

Show full item record

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

Item Usage Stats

101
views

0
downloads

The paper reports the development of a software platform, named PES (Parallelized Evolution System), that parallelizes the fitness evaluations of evolutionary methods over multiple computers connected via a network. The platform creates an infrastructure that allows the dispatching of fitness. evaluations onto a group of computers, running both Windows or Linux operating systems, parallelizing the evolutionary process. PES is based on the PVM (Parallel Virtual Machine) library and consists of two components; (1) a server component, named PES-Server, that executes the basic evolutionary method, the management of the communication with the client computers, and (2) a client component, named PES-Client, that executes programs to evaluate a single individual and return the fitness back to the server. Performance of PES is tested for the problem of evolving behaviors for a swarm of mobile robots simulated as physics-based models, and the speed-up characteristics are analyzed.

URI

https://hdl.handle.net/11511/55901

Journal

COMPUTER AND INFORMATION SCIENCES - ISCIS 2003

Collections

Department of Computer Engineering, Article

Suggestions

OpenMETU
Core

HotSprint: database of computational hot spots in protein interfaces Guney, Emre; Tunçbağ, Nurcan; Keskin, Ozlem; Gursoy, Attila (2008-01-01) We present a new database of computational hot spots in protein interfaces: HotSprint. Hot spots are residues comprising only a small fraction of interfaces yet accounting for the majority of the binding energy. HotSprint contains data for 35 776 protein interfaces among 49 512 protein interfaces extracted from the multi-chain structures in Protein Data Bank (PDB) as of February 2006. The conserved residues in interfaces with certain buried accessible solvent area (ASA) and complex ASA thresholds are flagge...
Interactive evolutionary multi-objective optimization for quasi-concave preference functions Fowler, John W.; Gel, Esma S.; Köksalan, Mustafa Murat; Korhonen, Pekka; Marquis, Jon L.; Wallenius, Jyrki (2010-10-16) We present a new hybrid approach to interactive evolutionary multi-objective optimization that uses a partial preference order to act as the fitness function in a customized genetic algorithm. We periodically send solutions to the decision maker (DM) for her evaluation and use the resulting preference information to form preference cones consisting of inferior solutions. The cones allow its to implicitly rank solutions that the DM has not considered. This technique avoids assuming an exact form for the pref...
Face recognition using Eigenfaces and neural networks Akalın, Volkan; Severcan, Mete; Department of Electrical and Electronics Engineering (2003) A face authentication system based on principal component analysis and neural networks is developed in this thesis. The system consists of three stages; preprocessing, principal component analysis, and recognition. In preprocessing stage, normalization illumination, and head orientation were done. Principal component analysis is applied to find the aspects of face which are important for identification. Eigenvectors and eigenfaces are calculated from the initial face image set. New faces are projected onto ...
Binary Classification Performance Measures/Metrics: A Comprehensive Visualized Roadmap to Gain New Insights Canbek, Gurol; SAĞIROĞLU, Şeref; Taşkaya Temizel, Tuğba; Baykal, Nazife (2017-10-08) Binary classification is one of the most frequent studies in applied machine learning problems in various domains, from medicine to biology to meteorology to malware analysis. Many researchers use some performance metrics in their classification studies to report their success. However, the literature has shown a widespread confusion about the terminology and ignorance of the fundamental aspects behind metrics. This paper clarifies the confusing terminology, suggests formal rules to distinguish between meas...
Geospatial Object Detection Using Deep Networks Barut, Onur; Alatan, Abdullah Aydın (2019-01-01) In the last decade, deep learning has been drawing a huge interest due to the developments in the computational hardware and novel machine learning techniques. This progress also significantly effects satellite image analysis for various objectives, such as disaster and crisis management, forest cover, road mapping, city planning and even military purposes. For all these applications, detection of geospatial objects has crucial importance and some recent object detection techniques are still unexplored to b...

Citation Formats

O. Soysal, E. Bahceci, and E. Şahin, “PES: A system for parallelized fitness evaluation of evolutionary methods,” COMPUTER AND INFORMATION SCIENCES - ISCIS 2003, pp. 900–907, 2003, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/55901.