Lecture Slides
... computational intelligence based techniques. The term AI was first time used in 1956 by John McCarthy. The term Computational Intelligence (CI) was first time used in 1994 to mainly cover areas such as neural networks, evolutionary algorithms and fuzzy logic. In this lecture we will focus only o ...
... computational intelligence based techniques. The term AI was first time used in 1956 by John McCarthy. The term Computational Intelligence (CI) was first time used in 1994 to mainly cover areas such as neural networks, evolutionary algorithms and fuzzy logic. In this lecture we will focus only o ...
An Evolutionary Artificial Neural Network Time Series Forecasting
... conventional methods. Short terms errors are smaller than the long ones, so there was no need to waste computational power. For long term forecasts the system results are even better (see table 5). ...
... conventional methods. Short terms errors are smaller than the long ones, so there was no need to waste computational power. For long term forecasts the system results are even better (see table 5). ...
Swarm intelligence
... ◦ Goal = Find the Shortest Path from Start to Finish Achieve this by designing rules to be run in parallel with one another (heuristics) We can consider each individual connection We can abandon each route that fails to meet the given rules ...
... ◦ Goal = Find the Shortest Path from Start to Finish Achieve this by designing rules to be run in parallel with one another (heuristics) We can consider each individual connection We can abandon each route that fails to meet the given rules ...
Heliostat Field Layout Optimization with Evolutionary Algorithms
... • The free variable method follows a more classical optimization approach by directly optimizing the x-y coordinates. Due to the complexity of the problem an appropriate heuristic is needed. There exists a large variety of optimization approaches which could be used, such as non-linear programming, ...
... • The free variable method follows a more classical optimization approach by directly optimizing the x-y coordinates. Due to the complexity of the problem an appropriate heuristic is needed. There exists a large variety of optimization approaches which could be used, such as non-linear programming, ...
1 What is Artificial Intelligence ( AI )
... machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approac ...
... machines as it is for human beings. It includes generation of new pieces of knowledge from given knowledge base, setting dynamic data structures for existing knowledge, learning knowledge from the environment and refinement of knowledge. Automated acquisition of knowledge by machine learning approac ...
A Review of Population-based Meta-Heuristic
... Many meta-heuristic algorithms have been proposed so far as shown in Table 1. Genetic Algorithm (GA) as a population-based meta-heuristic algorithm was suggested by Holland [42]. In the algorithm, a population of strings called chromosomes encodes candidate solutions for optimization problems. Simul ...
... Many meta-heuristic algorithms have been proposed so far as shown in Table 1. Genetic Algorithm (GA) as a population-based meta-heuristic algorithm was suggested by Holland [42]. In the algorithm, a population of strings called chromosomes encodes candidate solutions for optimization problems. Simul ...
Genetic algorithm
In the field of artificial intelligence, a genetic algorithm (GA) is a search heuristic that mimics the process of natural selection. This heuristic (also sometimes called a metaheuristic) is routinely used to generate useful solutions to optimization and search problems. Genetic algorithms belong to the larger class of evolutionary algorithms (EA), which generate solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection, and crossover.