8,273 research outputs found
Evolving Neural Networks through a Reverse Encoding Tree
NeuroEvolution is one of the most competitive evolutionary learning
frameworks for designing novel neural networks for use in specific tasks, such
as logic circuit design and digital gaming. However, the application of
benchmark methods such as the NeuroEvolution of Augmenting Topologies (NEAT)
remains a challenge, in terms of their computational cost and search time
inefficiency. This paper advances a method which incorporates a type of
topological edge coding, named Reverse Encoding Tree (RET), for evolving
scalable neural networks efficiently. Using RET, two types of approaches --
NEAT with Binary search encoding (Bi-NEAT) and NEAT with Golden-Section search
encoding (GS-NEAT) -- have been designed to solve problems in benchmark
continuous learning environments such as logic gates, Cartpole, and Lunar
Lander, and tested against classical NEAT and FS-NEAT as baselines.
Additionally, we conduct a robustness test to evaluate the resilience of the
proposed NEAT algorithms. The results show that the two proposed strategies
deliver improved performance, characterized by (1) a higher accumulated reward
within a finite number of time steps; (2) using fewer episodes to solve
problems in targeted environments, and (3) maintaining adaptive robustness
under noisy perturbations, which outperform the baselines in all tested cases.
Our analysis also demonstrates that RET expends potential future research
directions in dynamic environments. Code is available from
https://github.com/HaolingZHANG/ReverseEncodingTree.Comment: Accepted to IEEE Congress on Evolutionary Computation (IEEE CEC)
2020. Lecture Presentatio
Metaheuristic design of feedforward neural networks: a review of two decades of research
Over the past two decades, the feedforward neural network (FNN) optimization has been a key interest among the researchers and practitioners of multiple disciplines. The FNN optimization is often viewed from the various perspectives: the optimization of weights, network architecture, activation nodes, learning parameters, learning environment, etc. Researchers adopted such different viewpoints mainly to improve the FNN's generalization ability. The gradient-descent algorithm such as backpropagation has been widely applied to optimize the FNNs. Its success is evident from the FNN's application to numerous real-world problems. However, due to the limitations of the gradient-based optimization methods, the metaheuristic algorithms including the evolutionary algorithms, swarm intelligence, etc., are still being widely explored by the researchers aiming to obtain generalized FNN for a given problem. This article attempts to summarize a broad spectrum of FNN optimization methodologies including conventional and metaheuristic approaches. This article also tries to connect various research directions emerged out of the FNN optimization practices, such as evolving neural network (NN), cooperative coevolution NN, complex-valued NN, deep learning, extreme learning machine, quantum NN, etc. Additionally, it provides interesting research challenges for future research to cope-up with the present information processing era
Ms Pac-Man versus Ghost Team CEC 2011 competition
Games provide an ideal test bed for computational intelligence and significant progress has been made in recent years, most notably in games such as Go, where the level of play is now competitive with expert human play on smaller boards. Recently, a significantly more complex class of games has received increasing attention: real-time video games. These games pose many new challenges, including strict time constraints, simultaneous moves and open-endedness. Unlike in traditional board games, computational play is generally unable to compete with human players. One driving force in improving the overall performance of artificial intelligence players are game competitions where practitioners may evaluate and compare their methods against those submitted by others and possibly human players as well. In this paper we introduce a new competition based on the popular arcade video game Ms Pac-Man: Ms Pac-Man versus Ghost Team. The competition, to be held at the Congress on Evolutionary Computation 2011 for the first time, allows participants to develop controllers for either the Ms Pac-Man agent or for the Ghost Team and unlike previous Ms Pac-Man competitions that relied on screen capture, the players now interface directly with the game engine. In this paper we introduce the competition, including a review of previous work as well as a discussion of several aspects regarding the setting up of the game competition itself. © 2011 IEEE
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