Evolutionary algorithms (EAs) are very popular tools to design and evolve artificial neural networks (ANNs), especially to train them. These methods have advantages over the conventional backpropagation (BP) method because of their low computational requirement when searching in a large solution space. In this paper, we employ Chemical Reaction Optimization (CRO), a newly developed global optimization method, to replace BP in training neural networks. CRO is a population-based metaheuristics mimicking the transition of molecules and their interactions in a chemical reaction. Simulation results show that CRO outperforms many EA strategies commonly used to train neural networks.
Angeline proposed GeNeralized Acquisition of Recurrent Links (GNARL) using hybrid-GA to train ANNs @cite_15 . Instead of using symmetric topology, GNARL employs sparse connections of neural networks to represent the network structures. GNARL uses a mutation operation to evolve the structure and to tune the weights of networks. GNARL reserves the top 50 A Constructive algorithm for training Cooperative Neural Network Ensembles (CNNE), proposed by M. Islam @cite_20 , uses a constructive algorithm to evolve neural networks. CNNE relies on the contribution of individuals in the population and uses incremental learning to maintain the diversity among individuals in an ensemble. Incremental learning based on negative correlation could effectively reduce the redundancy generated by individuals searching the same solution space and thus different individuals could learn different aspect of the training data, which could result in a final solution of the ensemble. CNNE is a noninvasive" method which relies on proper implementation of BP. Though CNNE minimizes optimization problems by utilization of ensembles, it suffers from the structural climbing problem" @cite_15 .
@cite_15 @cite_20
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Standard methods for simultaneously inducing the structure and weights of recurrent neural networks limit every task to an assumed class of architectures. Such a simplification is necessary since the interactions between network structure and function are not well understood. Evolutionary computations, which include genetic algorithms and evolutionary programming, are population-based search methods that have shown promise in many similarly complex tasks. This paper argues that genetic algorithms are inappropriate for network acquisition and describes an evolutionary program, called GNARL, that simultaneously acquires both the structure and weights for recurrent networks. GNARL's empirical acquisition method allows for the emergence of complex behaviors and topologies that are potentially excluded by the artificial architectural constraints imposed in standard network induction methods. > Presents a constructive algorithm for training cooperative neural-network ensembles (CNNEs). CNNE combines ensemble architecture design with cooperative training for individual neural networks (NNs) in ensembles. Unlike most previous studies on training ensembles, CNNE puts emphasis on both accuracy and diversity among individual NNs in an ensemble. In order to maintain accuracy among individual NNs, the number of hidden nodes in individual NNs are also determined by a constructive approach. Incremental training based on negative correlation is used in CNNE to train individual NNs for different numbers of training epochs. The use of negative correlation learning and different training epochs for training individual NNs reflect CNNEs emphasis on diversity among individual NNs in an ensemble. CNNE has been tested extensively on a number of benchmark problems in machine learning and neural networks, including Australian credit card assessment, breast cancer, diabetes, glass, heart disease, letter recognition, soybean, and Mackey-Glass time series prediction problems. The experimental results show that CNNE can produce NN ensembles with good generalization ability.