A genetic programming methodology for missile countermeasures optimization under uncertainty

Abstract

This paper describes a new methodology for using genetic programming to solve the missile countermeasures optimization problem. The resulting system evolves programs that combine maneuvers with additional countermeasures to optimize aircraft survivability under conditions of uncertainty.

An optimal approach for generation of time delay algorithms for anti-air missiles

“…The GP approach has been studied for the time delay problem, firstly, because it does not rely on human effort or understanding of the complexities of the problem. Secondly, GP is not known to have been previously applied to this type of problem, although a previous study [4] applied it to improve aircraft survivability in an environment with surface-air missiles. Thirdly, GP is also particularly suitable because it is able to evolve explicit analytical expressions that can be postexamined for understandability after the learning process, unlike alternative learning algorithms such as neural networks.…”

Evasive Maneuver Strategy for UCAV in Beyond-Visual-Range Air Combat Based on Hierarchical Multi-Objective Evolutionary Algorithm

“…Some scholars have tried to use the intelligent optimization algorithm to solve the global optimal solution of the problem, because it opens up the possibility to search for an optimal solution with the presence of nonlinearity, parameter discontinuity, and discrete input [16]. Moore and Garcia [17], [18] described the implementation of a genetic programming system that evolved optimized solutions to the pursuer/evader problems. Nusyirwan and Bil [16], [19] proposed a technique using a parallel evolutionary algorithm to search for optimal control for an evasive fighter.…”