Mono-Objective Function Analysis Using an Optimization Approach
In this paper we propose an evolutionary technique based in a Lyapunov method for mono-objective optimization, that associate to every ergodic controllable finite Markov Chains a Lyapunov-like mono-objective function. For representing the trajectory dynamics properties local-optimal policies are def...
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2014 |
| País: | México |
| Institución: | Instituto Politécnico Nacional |
| Repositorio: | Repositorio Digital del IPN |
| OAI Identifier: | oai:www.repositoriodigital.ipn.mx:123456789/19796 |
| Acceso en línea: | http://www.repositoriodigital.ipn.mx/handle/123456789/19796 |
| Access Level: | acceso abierto |
| Palabra clave: | Lyapunov problem solving control methods search heuristic methods artificial intelligence |
| Sumario: | In this paper we propose an evolutionary technique based in a Lyapunov method for mono-objective optimization, that associate to every ergodic controllable finite Markov Chains a Lyapunov-like mono-objective function. For representing the trajectory dynamics properties local-optimal policies are defined to minimize the one-step decrement of the cost-function. We propose a state-value function that increase and decrease between states of the Markov decision processes. Then, we show that a Lyapunov mono-objective function, which can only decrease over time, can be built for the system. For illustration purposes, we present a simulated experiment that shows the trueness of the suggested method. |
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