Automatic definition of modular neural networks
Adaptive Behavior
The design and analysis of a computational model of cooperative coevolution
The design and analysis of a computational model of cooperative coevolution
Symbiotic evolution of neural networks in sequential decision tasks
Symbiotic evolution of neural networks in sequential decision tasks
Advances in genetic programming
Designing Neural Networks using Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Cellular Encoding Applied to Neurocontrol
Proceedings of the 6th International Conference on Genetic Algorithms
A Cooperative Coevolutionary Approach to Function Optimization
PPSN III Proceedings of the International Conference on Evolutionary Computation. The Third Conference on Parallel Problem Solving from Nature: Parallel Problem Solving from Nature
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Forming neural networks through efficient and adaptive coevolution
Evolutionary Computation
A comparison between cellular encoding and direct encoding for genetic neural networks
GECCO '96 Proceedings of the 1st annual conference on Genetic and evolutionary computation
Visual learning by coevolutionary feature synthesis
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Solving the pole balancing problem by means of assembler encoding
Journal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology
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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.