Abstract
Decomposing a complex computational problem into sub-problems, which are computationally simpler to solve individually and which can be combined to produce a solution to the full problem, can efficiently lead to compact and general solutions. Modular neural networks represent one of the ways in which this divide-and-conquer strategy can be implemented. Here we present a co-evolutionary model which is used to design and optimize modular neural networks with task-specific modules. The model consists of two populations. The first population consists of a pool of modules and the second population synthesizes complete systems by drawing elements from the pool of modules. Modules represent a part of the solution, which co-operates with others in the module population to form a complete solution. With the help of two artificial supervised learning tasks created by mixing two sub-tasks we demonstrate that if a particular task decomposition is better in terms of performance on the overall task, it can be evolved using this co-evolutionary model. © 2005 IEEE.
Original language | English |
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Title of host publication | The 2005 IEEE Congress on Evolutionary Computation, IEEE CEC 2005 |
Publisher | IEEE |
Pages | 2691-2698 |
Number of pages | 8 |
Volume | 3 |
ISBN (Print) | 0780393635 |
DOIs | |
Publication status | Published - 2005 |
Externally published | Yes |