Feature computation for BCI applications: A general purpose approach using a genetic algorithm. Preliminary results

2013 ◽  
Author(s):  
S. Ramat ◽  
N. Caramia
Keyword(s):  
Genetic Algorithm ◽  
General Purpose ◽  
Preliminary Results

Embodiment of Evolutionary Computation in General Agents

2000 ◽  
Vol 8 (4) ◽  
pp. 475-493 ◽  
Author(s):  
Robert E. Smith ◽  
Claudio Bonacina ◽  
Paul Kearney ◽  
Walter Merlat
Keyword(s):  
Evolutionary Computation ◽  
Large Scale ◽  
Software Agents ◽  
General Purpose ◽  
Universal System ◽  
Preliminary Results ◽  
Large Numbers ◽  
General Network ◽  
Large Scale Networks ◽  
General Purpose Software

Holland's Adaptation in Natural and Artificial Systems largely dealt with how systems, comprised of many self-interested entities, can and should adapt as a whole. This seminal book led to the last 25 years of work in geneticalgorithms (GAs) and related forms of evolutionary computation (EC). In recent years, the expansion of the Internet, other telecommunications technologies, and other large scale networks have led to a world where large numbers of semi-autonomous software entities (i.e., agents) will be interacting in an open, universal system. This development cast the importance of Holland's legacy in a new light. This paper argues that Holland's fundamental arguments, and the years of developments that have followed, have a direct impact on systems of general network agents, regardless of whether they explicitly exploit EC. However, it also argues that the techniques and theories of EC cannot be directly transferred to the world of general agents (rather than EC-specific) without examination of effects that are embodied in general software agents. This paper introduces a framework for EC interchanges between general-purpose software agents. Preliminary results are shown that illustrate the EC effects of asynchronous actions of agents within this framework. Building on this framework, coevolutionary agents that interact in a simulated producer/consumer economy are introduced. Using these preliminary results as illustrations, areas for future investigation of embodied EC software agents are discussed.

Customizable FPGA IP Core Implementation of a General-Purpose Genetic Algorithm Engine

2010 ◽  
Vol 14 (1) ◽  
pp. 133-149 ◽  
Author(s):  
P.R. Fernando ◽  
S. Katkoori ◽  
D. Keymeulen ◽  
R. Zebulum ◽  
A. Stoica
Keyword(s):  
Genetic Algorithm ◽  
General Purpose ◽  
Ip Core

scholarly journals A Neural Network: Family Competition Genetic Algorithm and Its Applications in Electromagnetic Optimization

2009 ◽  
Vol 2009 ◽  
pp. 1-12 ◽  
Author(s):  
P.-Y. Chen ◽  
C.-H. Chen ◽  
H. Wang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Optimization Problems ◽  
Computation Time ◽  
General Purpose ◽  
Optimization Process ◽  
Electromagnetic Properties ◽  
Design Parameters ◽  
Pass Filter ◽  
Low Pass Filter

This study proposes a neural network-family competition genetic algorithm (NN-FCGA) for solving the electromagnetic (EM) optimization and other general-purpose optimization problems. The NN-FCGA is a hybrid evolutionary-based algorithm, combining the good approximation performance of neural network (NN) and the robust and effective optimum search ability of the family competition genetic algorithms (FCGA) to accelerate the optimization process. In this study, the NN-FCGA is used to extract a set of optimal design parameters for two representative design examples: the multiple section low-pass filter and the polygonal electromagnetic absorber. Our results demonstrate that the optimal electromagnetic properties given by the NN-FCGA are comparable to those of the FCGA, but reducing a large amount of computation time and a well-trained NN model that can serve as a nonlinear approximator was developed during the optimization process of the NN-FCGA.

Some Preliminary Results on the Development and Comparison of a New Multi-Objective Genetic Algorithm

2001 ◽  
Author(s):  
A. Farhang-Mehr ◽  
J. Wu ◽  
S. Azarm
Keyword(s):  
Genetic Algorithm ◽  
Optimization Problem ◽  
Quality Metrics ◽  
Initial Population ◽  
Multi Objective Optimization ◽  
Pareto Solutions ◽  
Preliminary Results ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm ◽  
Speed Reducer

Abstract Some preliminary results for a new multi-objective genetic algorithm (MOGA) are presented. This new algorithm aims at obtaining the fullest possible representation of observed Pareto solutions to a multi-objective optimization problem. The algorithm, hereafter called entropy-based MOGA (or E-MOGA), is based on an application of the concepts from the statistical theory of gases to a MOGA. A few set quality metrics are introduced and used for a comparison of the E-MOGA to a previously published MOGA. Due to the stochastic nature of the MOGA, confidence intervals with a 95% confidence level are calculated for the quality metrics based on the randomness in the initial population. An engineering example, namely the design of a speed reducer is used to demonstrate the performance of E-MOGA when compared to the previous MOGA.

2208 Study on Development of General-purpose Genetic Algorithm Workbench

2001 ◽  
Vol 2001.11 (0) ◽  
pp. 134-136
Author(s):  
Harunobu KAWADA ◽  
Hirokazu NISHI ◽  
Amane SASAKI ◽  
Keishi KAWAMO
Keyword(s):  
Genetic Algorithm ◽  
General Purpose

scholarly journals Minimising Total Flowtime in a No-Wait Flow Shop (NWFS) using Genetic Algorithms

2018 ◽  
Vol 38 (3) ◽  
pp. 68-79 ◽  
Author(s):  
Imran Ali Chaudhry ◽  
Isam AbdulQader Elbadawi ◽  
Muhammad Usman ◽  
Muhammad Tajammal Chughtai
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Flow Shop ◽  
General Purpose ◽  
Flow Shop Scheduling ◽  
Benchmark Problems ◽  
Shop Scheduling ◽  
Total Flowtime ◽  
Spreadsheet Model ◽  
No Wait

This paper considers a no-wait flow shop scheduling (NWFS) problem, where the objective is to minimise the total flowtime. We propose a genetic algorithm (GA) that is implemented in a spreadsheet environment. The GA functions as an add-in in the spreadsheet. It is demonstrated that with proposed approach any criteria can be optimised without modifying the GA routine or spreadsheet model. Furthermore, the proposed method for solving this class of problem is general purpose, as it can be easily customised by adding or removing jobs and machines. Several benchmark problems already published in the literature are used to demonstrate the problem-solving capability of the proposed approach. Benchmark problems set ranges from small (7-jobs, 7 machines) to large (100-jobs, 10-machines). The performance of the GA is compared with different meta-heuristic techniques used in earlier literature. Experimental analysis demonstrate that solutions obtained in this research offer equal quality as compared to algorithms already developed for NWFS problems.

Genetically Engineered Nanostructure Devices

1998 ◽  
Vol 551 ◽  
Author(s):  
Gerhard Klimeck ◽  
Carlos H. Salazar-Lazaro ◽  
Adrian Stoica ◽  
Thomas Cwik
Keyword(s):  
Genetic Algorithm ◽  
Electron Transport ◽  
Quantum Well ◽  
Field Effect Transistors ◽  
General Purpose ◽  
Genetically Engineered ◽  
Atomic Scale ◽  
Quantum Mechanical ◽  
Random Structure ◽  
Analysis Tool

AbstractMaterial variations on an atomic scale enable the quantum mechanical functionality of devices such as resonant tunneling diodes (RTDs), quantum well infrared photodetectors (QWIPs), quantum well lasers, and heterostructure field effect transistors (HFETs). The design and optimization of such heterostructure devices requires a detailed understanding of quantum mechanical electron transport. The Nanoelectronic Modeling Tool (NEMO) is a general-purpose quantum device design and analysis tool that addresses this problem. NEMO was combined with a parallelized genetic algorithm package (PGAPACK) to optimize structural and material parameters. The electron transport simulations presented here are based on a full band simulation, including effects of non-parabolic bands in the longitudinal and transverse directions relative to the electron transport and Hartree charge self-consistency. The first result of the genetic algorithm driven quantum transport calculation with convergence of a random structure population to experimental data is presented.

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