A Hybrid Genetic Algorithm and Radial Basis Function NEAT
MetadataShow full item record
We propose a new neuroevolution technique that makes use of genetic algorithm to improve the task provided to a Radial Basis Function - NEAT algorithm. Normally, Radial Basis Function works best when the input- output mapping is smooth, that is, the dimensionality is high. However, if the input changes abruptly, for example, for fractured problems, efficient mapping cannot happen. Thus, the algorithm cannot solve such problems effectively. We make use of genetic algorithm to emulate the smoothing parameter in the Radial Basis function. In the proposed algorithm, the input- output mapping is done in a more efficient manner due to the ability of genetic algorithm to approximate almost any function. The technique has been successfully applied in the non- Markovian double pole balancing without velocity and the car racing strategy. It is shown that the proposed technique significantly outperforms classical neuroevolution techniques in both of the above benchmark problems.
Showing items related by title, author, creator and subject.
Cepuritis, Peter (2012)A methodology has been described that is capable of generating realistic 3-dimensional models of large-scale structures from mapped discontinuity trace data. The technique incorporates a search algorithm to hierarchically ...
Extraction and Optimization of Fuzzy Protein Sequences Classification Rules Using GRBF Neural NetworksWang, D.; Lee, N.; Dillon, Tharam S. (2003)Traditionally, two protein sequences are classified into the same class if their feature patterns have high homology. These feature patterns were originally extracted by sequence alignment algorithms, which measure ...
Chai, Qinqin (2013)In this thesis, we develop new computational methods for three classes of dynamic optimization problems: (i) A parameter identification problem for a general nonlinear time-delay system; (ii) an optimal control problem ...