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Genetic Algorithm in Artificial Intelligence
Genetic algorithms are a heuristic search technique inspired by biological evolution to find optimized solutions to problems. The workflow involves initially generating a random population which is then evaluated based on a fitness function. Individuals are selected from the population based on their fitness for reproduction, with crossover and mutation occurring to create a new generation. This process is repeated until an optimal solution is found. Genetic algorithms have applications in fields like robotics, medicine, and computer gaming. They provide advantages like not requiring derivatives and being able to optimize both continuous and discrete functions, but also have limitations such as computational expense and not guaranteeing optimal solutions.
Genetic Algorithm in Artificial Intelligence
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- 2. M d .S h a f a y a t u l A h s a n I D : 1 4 2 0 0 0 4 1 2 M d . M u b a s s h i r R a i h a n I D : 1 4 2 0 0 0 7 1 2 M d . E f t h a k h a r U l A l a m I D : 1 4 2 0 0 0 5 1 2 Team Members
- 3. Introduction Definition Properties Applications Advantages Limitations Overview
- 4. Genetic Algorithm (GA)is a search-based optimization technique based on the principles of Genetics and Natural Selection. Optimization is the process of making something better. Introduction
- 5. What is Genetic Algorithm? A heuristicsearch technique used in computing and Artificial Intelligence to find optimized solutions to search problems using techniques inspired by evolutionary biology. Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on bio- inspired operators such as selection, crossover & mutation.
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- 8. FITNESS FUNCTION Initial Population FitnessFunction A good fitness function return better state for the next generation. Fitness Score: 24+23+20+11 = 78 Probabilities Of Population { (24/78) x 100 } = 31% { (23/78) x 100 } = 29% { (20/78) x 100 } = 26% { (11/78) x 100 } = 14% 24748552 32752411 24415124 32543213 24 23 20 11 31% 29% 26% 14%
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- 11. MUTATION Each location inthe bit string can besubject toa mutation with small randomprobability
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- 14. Applications of Genetic Algorithm Robotics:Path planning in robotic applications. Robotics involves human designers and engineers trying out all sorts of things in order to create useful machines that can do work for humans. Medical: Genetic Algorithms can be used throughout the medical field. The GAs can help develop treatment programs, optimize drug formulas, improve diagnostics. Plasma X-ray Spectra Analysis: X-ray spectroscopic analysis is a powerful tool for plasma diagnostics. Computer Gaming: Those who spend some of their time playing computer games (creating their own civilizations and evolving them) will often find themselves playing against sophisticated artificial intelligence the GAs instead of against other human players online. ~ Crossover: In Pokemon tv series there was a chracter called pikachu. Which evolved using crossover process with another character Ninja and the new product from this crossover is Ninjachu. ~ Mutation : Red-Hair, Blue Eyes, Immunity, MCR1 – Pain tolerance,
- 15. ADVANTAGES Does not requireany derivative information (which may not be available for many real-world problems). Is faster and more efficient as compared to the traditional methods. Has very good parallel capabilities. Optimizes both continuous and discrete functions and also multi-objective problems. Provides a list of “good” solutions and not just a single solution. . .
- 16. LIMITATIONS GAs are notsuited for all problems, especially problems which are simple and for which derivative information is available. Fitness value is calculated repeatedly which might be computationally expensive for some problems Being stochastic, there are no guarantees on the optimality or the quality of the solution. If not implemented properly, the GA may not converge to the optimal solution. . .
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