The Optimization versus Survival Problem and Its Solution by an Evolutionary Multi Objective Algorithm
DOI: 10.1007/978-3-642-17298-4_53 Conference: Simulated Evolution and Learning - 8th International Conference, SEAL 2010, Kanpur, India, December 1-4, 2010. Proceedings
Altruism may be found in sets (groups of solutions). In such cases, it may occur that individual/individuals degrade their chances of survival (with sacrifice in the extreme) to ensure survival of fitter individuals. The idea of altruism within group evolution is posed here as a multi objective problem. The aspiration of a group to survive (find an optimal solution) is posed versus the individual's aspiration to survive. In the paper, the problem is a trajectory planning problem with the dilemma producing a Pareto set for a decision maker to choose from. It is shown that if the decision maker is ready to forfeit some of the group members, optimality may be gained. Evolutionary multi objective algorithm is implemented in order to search for this optimal set.
Available from: Gideon Avigad
- "The question is to determine which of the strategies adopted by the boats, are optimal strategies. If a common multi-objective optimization is applied to this problem, a Pareto front associated with this specific vessel's strategy will be found (see ). This Pareto front represents the best strategies that may be adopted by the boats so as to counteract the strategy adopted by the vessel. "
[Show abstract] [Hide abstract]
ABSTRACT: While both games and Multi-Objective Optimization (MOO) have been studied extensively in the literature, Multi-Objective Games (MOGs) have received less research attention. Existing studies deal mainly with mathematical formulations of the optimum. However, a definition and search for the representation of the optimal set, in the multi objective space, has not been attended. More specifically, a Pareto front for MOGs has not been defined or searched for in a concise way. In this paper we define such a front and propose a set-based multi-objective evolutionary algorithm to search for it. The resulting front, which is shown to be a layer rather than a clear-cut front, may support players in making strategic decisions during MOGs. Two examples are used to demonstrate the applicability of the algorithm. The results show that artificial intelligence may help solve complicated MOGs, thus highlighting a new and exciting research direction.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.