Evolutionary algorithms (EAs) are particularly suited to solve problems for which there is not much information available. From this standpoint, estimation of distribution algorithms (EDAs), which guide the search by using probabilistic models of the population, have brought a new view to evolutionary computation. While solving a given problem with an EDA, the user has access to a set of models...

in this paper, we introduce the  probabilistic normed groups. among other results, we investigate the continuityof inner automorphisms of a group and the continuity of left and right shifts in probabilistic group-norm. we also study midconvex functions defined  on probabilistic normed groups and  give  some results about locally boundedness of such  functions.

In this study we investigate two approachees for aggregation behavior in swarm robotics systems: Evolutionary methods and probabilistic control. In first part, aggregation behavior is chosen as a case, where performance and scalability of aggregation behaviors of perceptron controllers that are evolved for a simulated swarm robotic system are systematically studied with different parameter sett...

In this paper, we define the concepts of modified intuitionistic probabilistic metric spaces, the property (E.A.) and  the common property (E.A.) in   modified  intuitionistic probabilistic metric spaces.Then, by the commonproperty (E.A.), we prove some common fixed point theorems in modified intuitionistic Menger probabilistic metric spaces satisfying an implicit relation.

Bio-inspired evolutionary algorithms are probabilistic search methods that mimic natural biological evolution. They show the behavior of the biological entities interacting locally with one another or with their environment to solve complex problems. This paper aims to analyze the most predominantly used bio-inspired optimization techniques that have been used for stock market prediction and he...

This paper introduces a Genetic Programming (GP) approach to automatically evolve control programs for walking robots. In contrast to earlier work, in which the evolution of gait control programs depended on the direct measurement of the quality of movements of simulated robots, in this paper a new method is presented that circumvents time consuming evaluations of control programs through the p...

An Adaptive Quantum-based Multi-criterion Evolutionary Algorithm called AQMEA is a new paradigm of decision making for complex systems. Quantum-based algorithms utilize a new representation for the smallest unit of information, called a Q-bit, for the probabilistic representation that is based on the concept of qubits. Evolutionary computing with Q-bit chromosomes has a better characteristic of...

We present a general criterion guaranteeing the stochastic convergence of a wide class of nonautonomous evolutionary algorithms used for finding the global minimum of a continuous function. This paper is an extension of paper [6], where autonomous case was presented. Our main tool here is a cocycle system defined on the space of probabilistic measures and its stability properties.

Classic studies proposed that stochastic variability ("noise") can drive biological fate switching, enhancing evolutionary success. Now, Ho et al. report that HIV's reactivation from dormant (latently infected) patient cells-the major barrier to an HIV cure-is inherently stochastic. Eradicating an incompletely inducible (probabilistic) viral phenotype will require inventive approaches.