Open AccessNeuroevolution of augmented topologies with difference-based mutation
Author(s) -
Vladimir Stanovov,
Ш А Ахмедова,
Eugene Semenkin
Publication year - 2021
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1047/1/012075
Subject(s) - neuroevolution , mutation , network topology , computer science , artificial neural network , set (abstract data type) , position (finance) , scheme (mathematics) , operator (biology) , topology (electrical circuits) , artificial intelligence , mathematics , mathematical optimization , operating system , mathematical analysis , biochemistry , chemistry , finance , repressor , combinatorics , transcription factor , economics , gene , programming language
This study proposes the modification of the neuroevolution of augmented topologies, namely the difference-based mutation operator. The difference-based mutation changes the weights of the neural network by combining the weights of several other networks at the position of the connections having same innovation numbers. The implemented neuroevolution algorithm allows backward connections and loops in the topology, and uses several mutation operators, including connections deletion. The algorithm is tested on a set of classification problems and a rotary inverted pendulum problem and compared to the same approach without difference-based mutation. The experimental results show that the proposed weight tuning scheme allows significant improvements of classification quality in several cases and finding better control algorithms.