Solution of multi-objective min-max and max-min games by evolution

Gideon Avigad*, Erella Eisenstadt, Valery Y. Glizer

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


In this paper, a multi-objective optimal interception problem is proposed and solved using a Multi-Objective Evolutionary Algorithm. The traditional setting of an interception engagement between pursuer and evader is targeted either at minimizing a miss distance for a given interception duration or at minimizing an interception time for a given miss distance. Such a setting overlooks an important aspect - the purpose of launching the evader in the first place. Naturally, the evader seeks to evade the pursuer (by keeping away from it), but what about hitting its target? In contrast with the traditional setting, in this paper a multi-objective game is played between a pursuer and an evader. The pursuer aims at keeping a minimum final distance between itself and the evader, which it attempts to keep away from its target. The evader, on the other hand, aims at coming as close as possible to a predefined target while keeping as far away as possible from the pursuer. Both players (pursuer and evader) utilize neural net controllers that evolve during the proposed evolutionary optimization. The game is shown to involve very interesting issues related to the decision-making process while the dilemmas of both opponents are taken into consideration.

Original languageEnglish
Title of host publicationEvolutionary Multi-Criterion Optimization - 7th International Conference, EMO 2013, Proceedings
Number of pages15
StatePublished - 2013
Externally publishedYes
Event7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013 - Sheffield, United Kingdom
Duration: 19 Mar 201322 Mar 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7811 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference7th International Conference on Evolutionary Multi-Criterion Optimization, EMO 2013
Country/TerritoryUnited Kingdom


  • Differential games
  • evolutionary algorithms
  • worst-case evolution


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