Evolutionary algorithms based on stochastic programming are proposed for learning of the optimum bid in auction markets. Sellers and buyers are attempting to learn their optimum bids that maximize their individual utility functions in the next round of the game. Examples of a second-price sealed-bid auction, and double auction markets, with random and merit-based matching processes are consider...

A Neuro-Fuzzy (NF) system is a combination of Artificial Neural Network (ANN) and Fuzzy Inference System (FIS) in such a way that ANN learning algorithms are used to determine the parameters of FIS. Potential interactions between connectionist learning systems and evolutionary search procedures have attracted lots of research work recently. There is no guarantee that the learning algorithm conv...

The purpose of this article is to review the literature on the topic of deterministic vehicle routing problem (VRP) and to give a review on the exact and approximate solution techniques. More specifically the approximate (meta-heuristic) solution techniques are classified into: tabu search, simulated annealing, genetic algorithm, evolutionary algorithm, hybrid algorithm, and Ant Colony Opti...

We outline a theory of evolutionary computation using a formal model of evolutionary computation--the Evolutionary Turing Machine--which is introduced as the extension of the Turing Machine model. Evolutionary Turing Machines provide a better and a more complete model for evolutionary computing than conventional Turing Machines, algorithms, and Markov chains. The convergence and convergence rat...

In this work, we consider the problem of procedural content generation for video game levels. Prior approaches have relied on evolutionary search (ES) methods capable gener- ating diverse levels, but procedure is slow, which problematic in real-time settings. Reinforcement learning (RL) has also been proposed to tackle same problem, and while level fast, training time can be prohibitively expen...

Evolutionary algorithms and hill-climbing search models are investigated to address the protein structure prediction problem. This is a well-known NP-hard problem representing one of the most important and challenging problems in computational biology. The pull move operation is engaged as the main local search operator in several approaches to protein structure prediction. The considered appro...

The ability of an evolutionary algorithm (EA) to adapt its search strategy during the optimization process is a central concept in evolutionary computation, because (i) the best setting of an EA is not known a priori for a given task, (ii) the optimal search strategy is normally not constant during the evolutionary process. Thinking in terms of search (or generating) distributions, population a...