in this paper, tender problems in an automobile company for procuring needed items from potential suppliers have been resolved by the learning algorithm q. in this case the purchaser with respect to proposals received from potential providers, including price and delivery time is proposed; order the needed parts to suppliers assigns. the buyer’s objective is minimizing the procurement costs thr...

The stock market has always been an attractive area for researchers since no method has been found yet to predict the stock price behavior precisely. Due to its high rate of uncertainty and volatility, it carries a higher risk than any other investment area, thus the stock price behavior is difficult to simulation. This paper presents a “data mining-based evolutionary fuzzy expert system” (DEFE...

In this paper we study how individual learning interacts with an evolutionary algorithm in its search for good solutions to the Busy Beaver problem. Two learning strategies, the Baldwin Effect and Lamarckian learning, are compared with an extensive set of experiments. Results show that the Baldwin Effect is less sensitive to specific issues concerning the definition of the learning model and it...

In this study, the performance of the algorithms of whale, Differential evolutionary, crow search, and Gray Wolf optimization were evaluated to operate the Golestan Dam reservoir with the objective function of meeting downstream water needs. Also, after defining the objective function and its constraints, the convergence degree of the algorithms was compared with each other and with the absolut...

This article characterizes the evolutionary algorithm approach to reinforcement learning in relation to the more standard, temporal diierence methods. We describe several research issues in reinforcement learning and discuss similarities and diierences in how they are addressed by the two methods. A short survey of evolutionary reinforcement learning systems and their successful applications is...

This paper links the theory of genetic algorithm (GA) learning to evolutionary game theory. It is shown that economic learning via genetic algorithms can be described as a speci"c form of an evolutionary game. It will be pointed out that GA learning results in a series of near Nash equilibria which during the learning process build up to "nally approach a neighborhood of an evolutionarily stabl...

The organizational algorithm is examined as a computational approach to representing interpersonal learning. The structure of the algorithm is introduced and described in context to the simple genetic algorithm. A comparison is made of the performance of both algorithms with respect to three different test functions: a simple single-peaked function, the standard Matlab “peaks” function (Mathwor...

We present a system that is able to learn descriptions of pictures with an evolutionary algorithm approach. The descriptions are programs in a turtle-graphics language and the described pictures are scenes from an environment with a robotarm acting in a blocks-world. A measure of similarity of pictures is presented which can be computed fast and supplies gradient information with regard to tran...

We propose novel ways of solving Reinforcement Learning tasks (that is, stochastic optimal control tasks) by hybridising Evolutionary Algorithms with methods based on value functions. We call our approach Population-Based Reinforcement Learning. The key idea, from Evolutionary Computation, is that parallel interacting search processes (in this case Reinforcement Learning or Dynamic Programming ...