Abstract :
Learning and evolution are two fundamental forms of adaptation. There has been a great interest in combining learning and evolution with artificial neural networks (ANNs) in recent years. This paper: 1) reviews different combinations between ANNs and evolutionary algorithms (EAs), including using EAs to evolve ANN connection weights, architectures, learning rules, and input features; 2) discusses different search operators which have been used in various EAs; and 3) points out possible future research directions. It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone
Keywords :
genetic algorithms; learning (artificial intelligence); neural nets; search problems; technological forecasting; connection weights; evolutionary algorithms; intelligent systems; learning; neural networks; search operators; Adaptive systems; Algorithm design and analysis; Artificial intelligence; Artificial neural networks; Competitive intelligence; Computer networks; Evolutionary computation; Intelligent networks; Intelligent systems; Transfer functions;
