<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>The paper presents a new Artificial Neural Network (ANN) encoding method called Assembler Encoding (AE). It assumes that the ANN is encoded in the form of a program (Assembler Encoding Program, AEP) of a linear organization and of a structure similar to the structure of a simple assembler program. The task of the AEP is to create a Connectivity Matrix (CM) which can be transformed into the ANN of any architecture. To create AEPs, and in consequence ANNs, genetic algorithms (GAs) are used. In addition to the outline of AE, the paper also presents a new AEP encoding method, i.e., the method used to represent the AEP in the form of a chromosome or a set of chromosomes. The proposed method assumes the evolution of individual components of AEPs, i.e., operations and data, in separate populations. To test the method, experiments in two areas were carried out, i.e., in optimization and in a predator-prey problem. In the first case, the task of AE was to create matrices which constituted a solution to the optimization problem. In the second case, AE was responsible for constructing neural controllers used to control artificial predators whose task was to capture a fast-moving prey.</p></abstract>

The paper presents a new Artificial Neural Network (ANN) encoding method called Assembler Encoding (AE). It assumes that the ANN is encoded in the form of a program (Assembler Encoding Program, AEP) of a linear organization and of a structure similar to the structure of a simple assembler program. The task of the AEP is to create a Connectivity Matrix (CM) which can be transformed into the ANN of any architecture. To create AEPs, and in consequence ANNs, genetic algorithms (GAs) are used. In addition to the outline of AE, the paper also presents a new AEP encoding method, i.e., the method used to represent the AEP in the form of a chromosome or a set of chromosomes. The proposed method assumes the evolution of individual components of AEPs, i.e., operations and data, in separate populations. To test the method, experiments in two areas were carried out, i.e., in optimization and in a predator-prey problem. In the first case, the task of AE was to create matrices which constituted a solution to the optimization problem. In the second case, AE was responsible for constructing neural controllers used to control artificial predators whose task was to capture a fast-moving prey.

Evolving Co-Adapted Subcomponents in Assembler Encoding

Institute of Radioelectronic Systems, Polish Naval Academy, ul. Śmidowicza 69, Gdynia, Poland

International Journal of Applied Mathematics and Computer Science

{"article-title":"Evolving Co-Adapted Subcomponents in Assembler Encoding"}

The paper presents a new Artificial Neural Network (ANN) encoding method called Assembler Encoding (AE). It assumes that the ANN is encoded in the form of a...

Evolving Co-Adapted Subcomponents in Assembler Encoding

Publié en ligne: 07 janv. 2008

Pages: 549 - 563

DOI: https://doi.org/10.2478/v10006-007-0045-9

Mots clés
neural networks, evolution, neuroevolution

This content is open access.

Evolving Co-Adapted Subcomponents in Assembler Encoding

Publié en ligne: 07 janv. 2008

Pages: 549 - 563

DOI: https://doi.org/10.2478/v10006-007-0045-9

Mots clésneural networks, evolution, neuroevolution

This content is open access.

Mots clés
neural networks, evolution, neuroevolution