Multi-objective orienteering problems (MO-OPs) are classical multi-objective routing and have received much attention in recent decades. This study seeks to solve MO-OPs through a problem-decomposition framework, that is, an MO-OP is decomposed into knapsack problem (MOKP) traveling salesman (TSP). The MOKP TSP then solved by evolutionary algorithm (MOEA) deep reinforcement learning (DRL) metho...

The most important component that can express a person’s mental condition is facial expressions. A human communicate around 55% of information non-verbally and the remaining 45% audibly. Automatic expression recognition (FER) has now become challenging task in surveying computers. Applications FER include understanding behavior humans monitoring moods psychological states. It even penetrates ot...

This paper introduces a new hybrid hill-climbing algorithm (HHC) for solving the Economic Dispatch (ED) problem. This algorithm solves the ED problems with a systematic search structure with a global search. It improves the results obtained from an evolutionary algorithm with local search and converges to the best possible solution that grabs the accuracy of the problem. The most important goal...

In this study, a new method has been proposed for rule extraction required for a fuzzy classification system using Cellular Learning Automata Based on Evolutionary Computing (CLA-EC) model. CLA-EC model is an evolutionary algorithm which is a result of the combination of a cellular learning automata with the concepts mentioned in evolutionary computing. It has been shown a higher applicability ...

This paper proposes a novel adaptive group organization cooperative evolutionary algorithm (AGOCEA) for TSK-type neural fuzzy networks design. The proposed AGOCEA uses group-based cooperative evolutionary algorithm and selforganizing technique to automatically design neural fuzzy networks. The group-based evolutionary divided populations to several groups and each group can evolve itself. In th...

In this paper we will examine the problem of learning an e cient fuzzy logic rule set for the control of the inverted pendulum (with nonlinear dynamics) using an evolutionary algorithm. In particular we compare a two layered rule set with a single fuzzy logic rule set. Furthermore we look at the e ect that di erent choices of objective function (in the evolutionary algorithm) have on the rule s...

A recently developed multimodal evolutionary algorithm [8] is used as engine for a learning classifier system. The evolutionary classifier is applied for two largely used data sets and the obtained results are compared with others of different algorithms. A noticeable gain over other computational techniques is that, besides the outcome for objects in the test set, the algorithm may also provid...