Optimization of Evolutionary Neural Networks Using Hybrid Learning Algorithms

Evolutionary artificial neural networks (EANNs) refer to a special class of artificial neural networks (ANNs) in which evolution is another fundamental form of adaptation in addition to learning. Evolutionary algorithms are used to adapt the connection weights, network architecture and learning algorithms according to the problem environment. Even though evolutionary algorithms are well known as efficient global search algorithms, very often they miss the best local solutions in the complex solution space. In this paper, we propose a hybrid meta-heuristic learning approach combining evolutionary learning and local search methods (using 1st and 2nd order error information) to improve the learning and faster convergence obtained using a direct evolutionary approach. The proposed technique is tested on three different chaotic time series and the test results are compared with some popular neuro-fuzzy systems and a recently developed cutting angle method of global optimization. Empirical results reveal that the proposed technique is efficient in spite of the computational complexity

Generative capacities of cellular automata codification for evolution of NN codification

Proceeding of: International Conference on Artificial Neural Networks. ICANN 2002, Madrid, Spain, August 28-30, 2002Automatic methods for designing artificial neural nets are desired to avoid the laborious and erratically human expert’s job. Evolutionary computation has been used as a search technique to find appropriate NN architectures. Direct and indirect encoding methods are used to codify the net architecture into the chromosome. A reformulation of an indirect encoding method, based on two bi-dimensional cellular automata, and its generative capacity are presented.Publicad

Balancing Selection Pressures, Multiple Objectives, and Neural Modularity to Coevolve Cooperative Agent Behavior

Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs.\ individual behavior has a strong impact on the ability of evolved teams of artificial agents to exhibit teamwork in challenging tasks. However, such research only made use of single-objective evolution. In contrast, when a multiobjective evolutionary algorithm is used, populations can be subject to individual-level objectives, team-level objectives, or combinations of the two. This paper explores the performance of cooperatively coevolved teams of agents controlled by artificial neural networks subject to these types of objectives. Specifically, predator agents are evolved to capture scripted prey agents in a torus-shaped grid world. Because of the tension between individual and team behaviors, multiple modes of behavior can be useful, and thus the effect of modular neural networks is also explored. Results demonstrate that fitness rewarding individual behavior is superior to fitness rewarding team behavior, despite being applied to a cooperative task. However, the use of networks with multiple modules allows predators to discover intelligent behavior, regardless of which type of objectives are used

Robust design of a reentry unmanned space vehicle by multifidelity evolution control

This paper addresses the preliminary robust design of a small-medium scale re-entry unmanned space vehicle. A hybrid optimization technique is proposed that couples an evolutionary multi-objective algorithm with a direct transcription method for optimal control problems. Uncertainties on the aerodynamic forces and vehicle mass are integrated in the design process and the hybrid algorithm searches for geometries that a) minimize the mean value of the maximum heat flux, b) maximize the mean value of the maximum achievable distance, and c) minimize the variance of the maximum heat flux. The evolutionary part handles the system design parameters of the vehicle and the uncertain functions, while the direct transcription method generates optimal control profiles for the re-entry trajectory of each individual of the population. During the optimization process, artificial neural networks are used to approximate the aerodynamic forces required by the direct transcription method. The artificial neural networks are trained and updated by means of a multi-fidelity, evolution control approach

Meta-Learning Evolutionary Artificial Neural Networks

In this paper, we present MLEANN (Meta-Learning Evolutionary Artificial Neural Network), an automatic computational framework for the adaptive optimization of artificial neural networks wherein the neural network architecture, activation function, connection weights; learning algorithm and its parameters are adapted according to the problem. We explored the performance of MLEANN and conventionally designed artificial neural networks for function approximation problems. To evaluate the comparative performance, we used three different well-known chaotic time series. We also present the state of the art popular neural network learning algorithms and some experimentation results related to convergence speed and generalization performance. We explored the performance of backpropagation algorithm; conjugate gradient algorithm, quasi-Newton algorithm and Levenberg-Marquardt algorithm for the three chaotic time series. Performances of the different learning algorithms were evaluated when the activation functions and architecture were changed. We further present the theoretical background, algorithm, design strategy and further demonstrate how effective and inevitable is the proposed MLEANN framework to design a neural network, which is smaller, faster and with a better generalization performance