In this paper, we present an evolutionary multiobjective learning model achieving positive synergy between the Inference System and the Rule Base in order to obtain simpler, more compact and still accurate linguistic fuzzy models by learning fuzzy inference operators together with Rule Base. The Multiobjective Evolutionary Algorithm proposed generates a set of Fuzzy Rule Based Systems with diff...

Decision Trees are a decision support tool that contains tree like graph of decisions and the possible consequences. They are commonly used in different real world scenarios ranging from operations research to classifying a specie in a phylum given its features. The Decision Tree is implemented using traditional ID3 algorithm as well as an evolutionary algorithm for learning decision trees in t...

In this paper, evolutionary and dynamic programming based reinforcement learning techniques are combined to form an unsupervised learning scheme for designing autonomous optimal fuzzy logic control systems. A messy genetic algorithm, and an advantage learning scheme are first compared as reinforcement learning paradigms. The messy genetic algorithm enables flexible coding of a fuzzy structure f...

in this paper an approach based on evolutionary algorithms to find pareto optimal pair of state and control for multi-objective optimal control problems (moocp)'s is introduced‎. ‎in this approach‎, ‎first a discretized form of the time-control space is considered and then‎, ‎a piecewise linear control and a piecewise linear trajectory are obtained from the discretized time-control space using ...

multi agent markov decision processes (mmdps), as the generalization of markov decision processes to the multi agent case, have long been used for modeling multi agent system and are used as a suitable framework for multi agent reinforcement learning. in this paper, a generalized learning automata based algorithm for finding optimal policies in mmdp is proposed. in the proposed algorithm, mmdp ...

This paper studies the characteristics of a new composite evolutionary computation algorithm in which genetic evolution, individual learning and social learning interact in NK fitness landscape. We derive conditions for effective social learning in static and dynamic environments using computer simulations of a model of the composite evolutionary algorithm. The conditions for static environment...

This paper explores how inductive machine learning can guide the breeding process of evolutionary algorithms for black-box function optimization. In particular, decision trees are used to identify the underlying characteristics of good and bad individuals, using the mined knowledge for wise breeding purposes. Inductive learning is complemented with statistical learning in order to define the br...

This paper presents an objective experimental comparative study between four algorithms: the Genetic Algorithm, the Fitness Prediction Genetic Algorithm, the Population Based Incremental Learning algorithm and the purposed method based on the Chromosome Appearance Probability Matrix. The comparative is done with a non subjective evaluation function. The main objective is to validate the efficie...

We demonstrate an approach to modelling the effects of certain parameters of platform game levels on the players’ experience of the game. A version of Super Mario Bros has been adapted for generation of parameterized levels, and experiments are conducted over the web to collect data on the relationship between level design parameters and aspects of player experience. These relationships have be...

We consider Internet-based Master-Worker computations, like SETI@home, where a master process sends tasks, across the Internet, to worker processes; workers execute and report back some result. However, these workers are not trustworthy and it might be at their best interest to report incorrect results. In such master-worker computations, the behavior and the best interest of the workers might ...