Multi- Objective Evolutionary Algorithms of Spiking Neural Network
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Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many real-world optimisation problems include several contradictory objectives. Rather than single optimisation, Multi-Objective Optimisation (MOO) can be utilised as a set of optimal solutions to solve these problems.In this book, Harmony Search (HS) and memetic approach were use...
Spiking neural network (SNN) plays an essential role in classification problems. Although there are many models of SNN, Evolving Spiking Neural Network (ESNN) is widely used in many recent research works. Evolutionary algorithms, mainly differential evolution (DE) have been used for enhancing ESNN algorithm. However, many real-world optimisation problems include several contradictory objectives. Rather than single optimisation, Multi-Objective Optimisation (MOO) can be utilised as a set of optimal solutions to solve these problems.In this book, Harmony Search (HS) and memetic approach were used to improve the performance of MOO with ESNN. Consequently, Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) was applied to improve ESNN structure and accuracy rates. Standard data sets from the UCI machine learning are used for evaluating the performance of this enhanced multi objective hybrid model. The experimental resultshave proved that the Memetic Harmony Search Multi-Objective Differential Evolution with Evolving Spiking Neural Network (MEHSMODE-ESNN) gives better results in terms of accuracy and network structure.
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