our ancestors without fear, worry and anxiety are not our ancestors, as organisms without defense systems were extinct. therefore, while behavior such as fear and anxiety are useful to prevent harm, the exaggerated form, that is, anxiety disorders, has more costs than benefits. likewise, while a panic attack is a normal and useful response in the face of life-threatening danger, a panic disorde...

Nearly 72 years ago, Wertheimer [1] pointed out the importance of perceptual grouping and organization in vision and listed several key factors, such as similarity, proximity, and good continuation, which lead to visual grouping. However, even to this day, many of the computational issues of perceptual grouping have remained unresolved. Since there are many possible partitions of an image into ...

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...

In this paper we present two advanced methods for evolutionary optimisation. One method is based on Parallel Genetic Algorithms. It is called Co-operating Populations with Different Evolution Behaviours (CoPDEB), and allows each population to exhibit a different evolution behaviour. Results from two problems show the advantage of using different evolution behaviour on each population. The other...

We discuss the use of genetic algorithms (GAs) for the generation of music. We explain the structure of a typical GA, and outline existing work on the use of GAs in computer music. We propose that the addition of domain-specific knowledge can enhance the quality and speed of production of GA results, and describe two systems which exemplify this. However, we conclude that GAs are not ideal for ...

In 1963 and 1964, L. L. Cavalli-Sforza and A. W. F. Edwards introduced novel methods for computing evolutionary trees from genetical data, initially for human populations from blood-group gene frequencies. The most important development was their introduction of statistical methods of estimation applied to stochastic models of evolution.

3.1 Fitness Proportionate Pelection (FPS) 3.2 Windowing 3.3 Sigma Scaling 3.4 Linear Scaling 3.5 Sampling Algorithms 3.6 Ranking 3.7 Linear Ranking 3.8 Exponential Ranking 3.9 Tournament Selection 3.10 Genitor or Steady State Models 3.11 Evolution Strategy and Evolutionary Programming Methods 3.12 Evolution Strategy Approaches 3.13 Top-n Selection 3.14 Evolutionary Programming Methods 3.15 The ...

Four multi-objective evolutionary optimization algorithms are discussed with respect to their efficiency in portfolio optimization problems. The assessment of the advantages and disadvantages of the considered algorithms is based on experimental study where two and three criteria portfolio optimization problems were used as tests. The performance of considered algorithms are presented and compa...