Genetic Algorithms: How Computers Learn Like Humans
In the world of computers, there’s one thing that has always set humans apart – the ability to learn and adapt to new situations. But as technology evolves, so does our ability to replicate human learning through Artificial Intelligence (AI). One such technique that has gained popularity for its ability to simulate evolution and mimic human learning is Genetic Algorithms (GA).
Starting with a brief history
Although the term “Genetic Algorithm” was coined by John Holland in 1975, the concept of the algorithmic technique was first introduced by Charles Darwin’s theory of natural selection. In fact, natural selection was the inspiration behind the concept of genetic algorithms. It was John Holland who brought the idea of mimicking natural selection algorithmically to solve engineering problems with the help of computers.
So, what are Genetic Algorithms?
Simply put, GA is a computational technique that uses a variation of Darwin’s theory of natural selection to solve complex problems. It works by taking some input, generating a population of possible solutions to the problem, allowing them to evolve and compete with each other, with the most successful ones being selected to reproduce and create the next generation of potential solutions. In this way, the algorithm is based on the concept of “survival of the fittest,” and thus, we can imitate the process of biological evolution.
Genetic Algorithm’s Model
The GA has a simple model, just as the natural selection model. The model is as follows:
1. Randomly generate an initial population of chromosomes. Each chromosome represents a potential solution to the problem at hand.
2. Evaluate the fitness of each chromosome. It is done by checking how well it performs against our objective function. It defines how to measure a good or bad solution.
3. Select the parents. The selected parents reproduce and recombine their genetic material to create the next generation of chromosomes.
4. Apply genetic operators. Genetic operators like mutation and crossover are applied to the next generation of chromosomes.
5. Evaluate the fitness. After applying genetic operators, we evaluate the fitness again.
6. Repeat the entire process. It is repeated until a satisfactory solution is found.
Iteratively, we repeat the process for several generations until satisfactory solutions are obtained.
Real-life examples of Genetic Algorithms
Given the diverse range of problems that can be solved through genetic algorithms, it has been applied in many industries, from finance to medicine, manufacturing to engineering. Let’s look at a few real-life examples of the Genetic Algorithm’s application:
1. Finding the shortest route between locations: The traveling salesman problem is a classic example of optimization problems, where we want to find the shortest route that visits all the locations and returns to the starting point. The GA can be used to generate and evolve potential solutions over time, selecting the best among them to minimize the distance being covered.
2. Determining a Stock Portfolio: One of the most challenging things for investment managers is to build a portfolio that balances potential risk and returns. The GA can optimize a stock portfolio, by selecting and weighting the assets to achieve the objective function, which is, to maximize expected returns with minimal risk.
3. Forecasting rainfall: Accurate rainfall prediction is essential to mitigate the impact of floods and droughts. Genetic algorithms use the historical weather data to predict future information, by selecting and evolving potential solutions iteratively that provide the most accurate forecast.
4. Designing Bridges: In engineering, GA can be used to optimize designs of structures like bridges, aircraft, or automobiles. It generates potential designs, allowing them to compete with each other based on fitness, and selecting the most optimized design for producing the final product.
How Genetic Algorithms make Computers Learn-Like Humans
Our brain is exceptional when it comes to learning, adapting, and evolving. But, how can we replicate that in machines like computers? The answer is through GA. The Genetic Algorithm uses computational models to simulate the principles of natural selection, allowing computers to think in terms of metaphorical chromosomes, genetic mutations, and genetic crossover operations. Using the simple concept of “fittest survive,” the algorithm iteratively evolves and adapts to create an optimized solution, just like humans.
Conclusion:
The Genetic Algorithm is a fascinating technique that has brought humans closer to mimicking evolution with computational models. The GA’s greatest strength lies in its ability to optimize solutions and handle a vast number of variables to solve complex problems. It has enabled industries to solve problems that were once impossible to solve with traditional methods. As we continue to develop AI and machine learning, Genetic Algorithms will have a significant role to play in AI’s future.