Continuous non-revisiting genetic algorithm

Abstract

The non-revisiting genetic algorithm (NrGA) is extended to handle continuous search space. The extended NrGA model, Continuous NrGA (cNrGA), employs the same tree-structure archive of NrGA to memorize the evaluated solutions, in which the search space is divided into non-overlapped partitions according to the distribution of the solutions. cNrGA is a bi-modulus evolutionary algorithm consisting of the genetic algorithm module (GAM) and the adaptive mutation module (AMM). When GAM generates an offspring, the offspring is sent to AMM and is mutated according to the density of the solutions stored in the memory archive. For a point in the search space with high solution-density, it infers a high probability that the point is close to the optimum and hence a near search is suggested. Alternatively, a far search is recommended for a point with low solution-density. Benefitting from the space partitioning scheme, a fast solution-density approximation is obtained. Also, the adaptive mutation scheme naturally avoid the generation of out-of-bound solutions. The performance of cNrGA is tested on 14 benchmark functions on dimensions ranging from 2 to 40. It is compared with real coded GA, differential evolution, covariance matrix adaptation evolution strategy and two improved particle swarm optimization. The simulation results show that cNrGA outperforms the other algorithms for multi-modal function optimization.

Continuous non-revisiting genetic algorithm with random search space re-partitioning and one-gene-flip mutation

“…non-revisiting genetic algorithm [1] (cNrGA) Test algorithm 2 -Continuous non-revisiting genetic algorithm with randomly re-partitioned BSP tree (cNrGA/RP/OGF). Test algorithm 3 -CMA-ES [5].…”

“…ontinuous Non-Revisiting GA (cNrGA) [1] is an extension of (discrete) NrGA [2] to continuous variables. It uses a binary space partitioning (BSP) tree archive, namely density tree, to record the positions of evaluated solutions as well as to represent the density distribution of the solutions.…”

A non-revisiting artificial bee colony algorithm for phased array synthesis

“…Yuen and Chow proposed to use binary space partitioning (BSP) tree to integrate with GA [35]. BSP tree stores all evaluated solutions by GA.…”

On solving Mincut Balanced Circuit Partitioning Problem for Digital Circuit Layout using Evolutionary Approach with Solution Archive

“…The min-cut balanced bipartition problem was shown to be NP-complete [13]. Because of its applicability in many areas, many heuristic algorithms have been devised for its solution.…”

“…To tackle with this problem attention was paid to various hybrid evolutionary approaches [2, 6,9,10,12,14] with local improvement for solving circuit partitioning problem in the recent years. This paper is based on the idea of tackling the problem of local convergence by avoiding the revisit of evaluated solutions .The proposed evolutionary approach takes the help of binary trie [4,13] to store the solutions encoded in the form of binary strings. In case of solution revisit, the algorithm further transforms the solutions into yet unconsidered candidate solutions.…”