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A genetic algorithm is used to solve complicated problems with a greater number of variables & possible outcomes/solutions. The combinations of different solutions are passed through the Darwinian based algorithm to find the best solutions. The poorer solutions are then replaced with the offspring of good solutions.
It all works on the Darwinian theory, where only the fittest individuals are chosen for reproduction. The various solutions are considered the elements of the population, and only the fittest solutions are allowed to reproduce (to create better solutions). Genetic algorithms help in optimizing the solutions to any particular problem.
The whole process of genetic algorithms is a computer program simulation in which the attributes of the problem & solution are treated as the attributes of the Darwinian theory. The basic processes which are involved in genetic algorithms are as follows:
- A population of solutions is built to any particular problem. The elements of the population compete with each other to find out the fittest one.
- The elements of the population that are fit are only allowed to create offspring (better solutions).
- The genes from the fittest parents (solutions) create a better offspring. Thus, future solutions will be better and sustainable.
Working of Genetic Algorithms in AI
The working of a genetic algorithm in AI is as follows:
- The components of the population, i.e., elements, are termed as genes in genetic algorithms in AI. These genes form an individual in the population (also termed as a chromosome).
- A search space is created in which all the individuals are accumulated. All the individuals are coded within a finite length in the search space.
- Each individual in the search space (population) is given a fitness score, which tells its ability to compete with other individuals.
- All the individuals with their respective fitness scores are sought & maintained by the genetic algorithm & the individuals with high fitness scores are given a chance to reproduce.
- The new offspring are having better ‘partial solutions’ as compared to their parents. Genetic algorithms also keep the space of the search space dynamic for accumulating the new solutions (offspring).
- This process is repeated until the offsprings do not have any new attributes/features than their parents (convergence). The population converges at the end, and only the fittest solutions remain along with their offspring (better solutions). The fitness score of new individuals in the population (offspring) are also calculated.
Key Terminologies in Genetic Algorithms
- Selection Operator – This operator in genetic algorithms in AI is responsible for selecting the individuals with better fitness scores for reproduction.
- Crossover Operator – The crossover operator chooses a crossover site from where the merge will happen. The crossover sites in both the individuals available for mating are chosen randomly and form new individuals.
- Mutation Operator – This operator in the genetic algorithm is responsible for embedding random genes in the offspring to maintain diversity and avoid premature convergence.
- Premature Convergence – If a problem is optimized quickly, it means that the offspring were not produced at many levels. The solutions will also not be of optimal quality. To avoid premature convergence, new genes are added by the mutation operator.
- Allele – The value of a particular gene in a chromosome is termed as an allele. The specified set of alleles for each gene defines the possible chromosomes of that particular gene.
Benefits and Uses of Genetic Algorithms
- The solutions created through genetic algorithms are strong & reliable as compared to other solutions.
- They increase the size of solutions as solutions can be optimized over a large search scale. This algorithm also can manage a large population.
- The solutions produced by genetic algorithms do not deviate much on slightly changing the input. They can handle a little bit of noise.
- Genetic algorithms have a stochastic distribution that follows probabilistic transition rules, making them hard to predict but easy to analyze.
- Genetic algorithms can also perform in noisy environments. It can also work in case of complex & discrete problems.
- Due to their effectiveness, genetic algorithms have many applications like neural networks, fuzzy logic, code-breaking, filtering & signal processing. You can learn more about the genetic algorithms in AI via the top courses offered by upGrad.
Also Read: Types of Classification Algorithm in ML
Why Learn from upGrad?
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- Master of Science in Machine Learning & AI affiliated to the Liverpool John Moores University (LJMU).
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Genetic algorithms are an important concept in AI and are one of the best performing AI algorithms when it comes to finding optimal solutions. You can know more about this topic by opting for the AI courses provided by upGrad taught by expert faculties. The LJMU course on upGrad is of 18 months, while the other is of 12 months. Start learning AI now!
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What do you mean by genetic algorithm, and how is it related to biology?
Algorithms, in general, are a set of rules or instructions which are to be followed by a computer or a device to execute a particular task or to solve a problem. The genetic algorithm is an evolutionary algorithm that is based on one of the biological theories of Charles Darwin. It's called the survival of the fittest, and according to this phrase, only the organisms who adjust best to their environment have the chances of survival and reproduction. Similar to the theory, the genetic algorithm is an iterative algorithm that finds a solution that is the best solution among the solutions available to the device. The chromosomes are represented by arrays of bits or characters in a genetic algorithm, which implements the computing paradigm. Each string corresponds to a possible solution. The genetic algorithm then tweaks the most promising chromosomes to pursue better results.
What are the real-life use cases of genetic algorithms?
By replicating the evolutionary process of survival of the fittest to attain a given goal, genetic algorithms potentially approach complex issues with numerous variables and a massive number of alternative solutions. In economics, genetic algorithms represent concepts including game theory, the cobweb model, asset pricing, and schedule optimization. They're also utilized to figure out how to make goods delivery more efficient. They're employed in DNA analysis to determine the DNA structure based on spectrometric data. In multimodal optimization issues, they are utilized to give numerous optimal solutions. They are employed in the creation of parametric aircraft designs. The aircraft's parameters are updated and enhanced using genetic algorithms to produce superior designs.
What are the limitations of the genetic algorithm?
Like any other algorithm, genetic algorithms also have their own set of limitations. The first limitation is that these algorithms are computationally expensive because the evaluation of each individual necessitates the training of a model. The second problem faced with these algorithms is that they are ineffective in tackling minor problems. Another issue with these algorithms is that their stochastic nature can take a long time to converge, and improper implementation may cause the algorithm to converge to an unsatisfactory result. Also, in a genetic algorithm, the quality of the final answer is not guaranteed. In these algorithms, the repetitive calculation of the fittest values may cause specific issues with computing hurdles.