E-Net: Evolutionary neural network synthesis
E-Net is a new distributed evolutionary learning system that evolves neural-network-based pattern recognition systems (PRSs) with limited human interaction. This system orchestrates a multiplicity of evolutionary and classical learning techniques to synthesize feature detectors, select sets of cooperative features, and assemble classifiers. Feature detectors are represented as feed-forward neural networks and recognition systems are defined using a collection of networks. E-Net evolves network topologies and trains weights to form accurate recognition systems using a computationally efficient process that gradually extends primitive network topologies to form increasingly discriminating structures. The evolutionary search process effectively explores the space of candidate topologies by manipulating populations of feature detectors and recognition systems using variation operators such as crossover and mutation. The majority of evolutionary learning techniques have been designed to perform parameter optimization. E-Net is designed to perform both synthesis and optimization. Consequently, many novel concepts and techniques are introduced in this research that expedite the gradual synthesis of structure, such as the new multitiered selection process used in E-Net's evolutionary algorithm that avoids premature convergence to complex topological structures.
neural networks evolutionary learning genetic algorithms