In this paper, Divided Range Genetic Algorithm in Multi objective optimization Problems (DRGA) is proposed. In this method, population of GAs is sorted with respect to the objective function and divided into sub populations. In this model, the Pareto optimum solutions which are close to each other are collected by one sub population. Therefore, by this algorithm, the calculation efficiency is i...

In this paper, Divided Range Genetic Algorithm in Multi objective optimization Problems (DRGA) is proposed. In this method, population of GAs is sorted with respect to the objective function and divided into sub populations. In this model, the Pareto optimum solutions which are close to each other are collected by one sub population. Therefore, by this algorithm, the calculation e ciency is inc...

OBJECTIVE To find out postoperative discomfort in children undergoing dental rehabilitation under general anesthesia (DRGA). METHODS This study involved 78 (4 to 10 year-old) healthy patients who were scheduled for DRGA and were needed extensive dental treatment because of severe caries, and showed high dental fear and/or behavioral management problems. The children had to be fit for DRGA adm...

Reinforcement learning often involves assuming Markov characteristics. However, the agent cannot always observe the environment completely, and in such cases, different states are observed as the same state. In this research, the authors develop a Delayed Reward-based Genetic Algorithm for POMDP (DRGA) as a means to solve a partially observable Markov decision problem (POMDP) which has such per...

How should we measure metacognitive ("type 2") sensitivity, i.e. the efficacy with which observers' confidence ratings discriminate between their own correct and incorrect stimulus classifications? We argue that currently available methods are inadequate because they are influenced by factors such as response bias and type 1 sensitivity (i.e. ability to distinguish stimuli). Extending the signa...

A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done in an ad-hoc fashion, requiring expert knowledge from heuristics designers, or extensive exploration of suitable combinations of heuristics. The approach here is to automate that selection ...