EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System

2013 ◽  
pp. 73-85 ◽  
Author(s):  
Una-May O’Reilly ◽  
Mark Wagy ◽  
Babak Hodjat
Keyword(s):  
Genetic Programming ◽  
Programming System ◽  
Hub And Spoke ◽  
Massive Scale

Genetic Programming for Robust Text Independent Speaker Verification

2010 ◽  
pp. 259-280
Author(s):  
Peter Day ◽  
Asoke K. Nandi
Keyword(s):  
Feature Selection ◽  
Genetic Programming ◽  
Speaker Verification ◽  
Telephone Network ◽  
Programming System ◽  
Security Measures ◽  
Inherent Feature ◽  
Text Independent Speaker Verification ◽  
Remote Security ◽  
Principal Advantage

Robust Automatic Speaker Verification has become increasingly desirable in recent years with the growing trend toward remote security verification procedures for telephone banking, bio-metric security measures and similar applications. While many approaches have been applied to this problem, Genetic Programming offers inherent feature selection and solutions that can be meaningfully analyzed, making it well suited for this task. This chapter introduces a Genetic Programming system to evolve programs capable of speaker verification and evaluates its performance with the publicly available TIMIT corpora. Also presented are the effects of a simulated telephone network on classification results which highlight the principal advantage, namely robustness to both additive and convolutive noise.

Listening to Data: Tuning a Genetic Programming System

2006 ◽  
pp. 245-262 ◽  
Author(s):  
Duncan MacLean ◽  
Eric A. Wollesen ◽  
Bill Worzel
Keyword(s):  
Genetic Programming ◽  
Programming System

scholarly journals Automating the Packing Heuristic Design Process with Genetic Programming

2012 ◽  
Vol 20 (1) ◽  
pp. 63-89 ◽  
Author(s):  
Edmund K. Burke ◽  
Matthew R. Hyde ◽  
Graham Kendall ◽  
John Woodward
Keyword(s):  
Genetic Programming ◽  
Design Process ◽  
Design Methodology ◽  
Bin Packing ◽  
Three Dimensional ◽  
Programming System ◽  
Problem Type ◽  
Packing Problems ◽  
Design Heuristics ◽  
Design Heuristic

The literature shows that one-, two-, and three-dimensional bin packing and knapsack packing are difficult problems in operational research. Many techniques, including exact, heuristic, and metaheuristic approaches, have been investigated to solve these problems and it is often not clear which method to use when presented with a new instance. This paper presents an approach which is motivated by the goal of building computer systems which can design heuristic methods. The overall aim is to explore the possibilities for automating the heuristic design process. We present a genetic programming system to automatically generate a good quality heuristic for each instance. It is not necessary to change the methodology depending on the problem type (one-, two-, or three-dimensional knapsack and bin packing problems), and it therefore has a level of generality unmatched by other systems in the literature. We carry out an extensive suite of experiments and compare with the best human designed heuristics in the literature. Note that our heuristic design methodology uses the same parameters for all the experiments. The contribution of this paper is to present a more general packing methodology than those currently available, and to show that, by using this methodology, it is possible for a computer system to design heuristics which are competitive with the human designed heuristics from the literature. This represents the first packing algorithm in the literature able to claim human competitive results in such a wide variety of packing domains.

GENETIC PROGRAMMING OF POLYNOMIAL HARMONIC NETWORKS USING THE DISCRETE FOURIER TRANSFORM

2002 ◽  
Vol 12 (05) ◽  
pp. 399-410
Author(s):  
NIKOLAY Y. NIKOLAEV ◽  
HITOSHI IBA
Keyword(s):  
Fourier Transform ◽  
Genetic Programming ◽  
Discrete Fourier Transform ◽  
Programming System ◽  
The Novel ◽  
Evolutionary System ◽  
Structural Search ◽  
Feed Forward Neural Networks ◽  
Multiple Frequencies ◽  
Polynomial Activation Functions

This paper presents a genetic programming system that evolves polynomial harmonic networks. These are multilayer feed-forward neural networks with polynomial activation functions. The novel hybrids assume that harmonics with non-multiple frequencies may enter as inputs the activation polynomials. The harmonics with non-multiple, irregular frequencies are derived analytically using the discrete Fourier transform. The polynomial harmonic networks have tree-structured topology which makes them especially suitable for evolutionary structural search. Empirical results show that this hybrid genetic programming system outperforms an evolutionary system manipulating polynomials, the traditional Koza-style genetic programming, and the harmonic GMDH network algorithm on processing time series.

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