We present Fitness Expectation Maximization (FEM), a novel method for performing ‘black box’ function optimization. FEM searches the fitness landscape of an objective function using an instantiation of the well-known Expectation Maximization algorithm, producing search points to match the sample distribution weighted according to higher expected fitness. FEM updates both candidate solution para...

Novel adaptations often cause pleiotropic reductions in fitness. Under optimal conditions individual organisms may be able to compensate for, or reduce, these fitness costs. Declining environmental quality may therefore lead to larger costs. We investigated whether reduced plant quality would increase the fitness costs associated with resistance to Bacillus thuringiensis in two populations of t...

A stochastic Lyapunov function was used to assess the convergence velocity of a simple evolutionary algorithm with self-adaptation, which searches for a maximum of a “fitness” function. This algorithm uses two types of parameters: “fitness” parameters belonging to the domain of the function, and strategy parameters, which control changes of fitness parameters. It was shown that the convergence ...

An adjustable fitness model for amino acid site substitutions is investigated. This model, a generalization of previously developed evolutionary models, has several distinguishing characteristics: it separately accounts for the processes of mutation and substitution, allows for heterogeneity among substitution rates and among evolutionary constraints, and does not make any prior assumptions abo...

Some domains, like robot soccer, are difficult for agents to learn in using direct statistical and reinforcement learning techniques. However, agents often have a goal or purpose, which gives them a natural basis for reinforcement learning in the form of a fitness function. Such a fitness function allows the task of learning to be accomplished through optimization of the fitness function in the...

Background modeling algorithms are commonly used in camera setups for foreground object detection. Typically, these algorithms need adjustment of their parameters towards achieving optimal performance in different scenarios and/or lighting conditions. This is a tedious process requiring considerable effort by expert users. In this work we propose a novel, fully automatic method for the tuning o...

The target position information contained in scattering data is explored in the context of the scalar Helmholtz operator for the basic twopoint scatterer system by means of the statistical estimation framework of the Fisher information and associated Cramér-Rao bound (CRB) relevant to unbiased position estimation. The CRB results are derived for the exact multiple scattering model and, for refe...

The objective of this paper is path planning of a 3 DOF planer robot with hydraulic actuator using genetic algorithm. First the geometric and kinematic parameters of robot were established. The equations of motion are derived by Lagrange method. We proposed the model for proportional valve and hydraulic actuators. Then using the genetic algorithm we minimized the hydraulic energy consumption as...

This article describes a method for selecting design parameters and a particular sequence of events in fMRI so as to maximize statistical power and psychological validity. Our approach uses a genetic algorithm (GA), a class of flexible search algorithms that optimize designs with respect to single or multiple measures of fitness. Two strengths of the GA framework are that (1) it operates with a...

BACKGROUND The present study was aimed at investigating the effects of sleep deprivation and dietary irregularities during Ramadan intermittent fasting (RIF) on selective fitness profile parameters in young untrained male Muslim individuals. METHODS 77 untrained Muslim men were recruited in the study. They were divided into the experimental group (EG; n=37, age: 22.62±1.77 years) and the cont...