In the increasingly complex modern world, many problems require optimal solutions, both business and technology and science. One of the many approaches used to solve problems is genetic algorithms. These algorithms are inspired by the process of natural evolution and work by imitating the principles of natural selection to find the best solution of a problem. This article will talk about how genetic algorithms are applied in global optimization, how it works, and what all the advantages and challenges of application.

Genetic algorithm understanding

Genetic algorithm is one of the methods of artificial intelligence developed based on the concept of biological evolution. This algorithm was first introduced by John Holland in 1975. The basic principle of genetic algorithms is survival of the fittest, where the best solutions of a population will be maintained and developed into a better solution. This algorithm operates on a set of candidate solutions, called a population, and produces optimal solutions through the recombination process, mutations and selections.

Genetic algorithms are very useful in finding optimal solutions to problems that can't be solved in conventional ways, especially for problems that have a lot of variables and a very wide space solutions. Because it works based on the principle of random search, it can often find solutions approaching optimal to complex problems.

Genetic Algorithm Work

The work of genetic algorithms consists of several stages, similar to the biological evolution process. Here are the steps:

1. Initialize Population
   The first step in the genetic algorithm is to create a population of individuals. Each individual is represented as a chromosome containing a series of genes. The genes here are parameters or variables to optimize. This initial population is usually generated randomly or based on the initial guesses of the user.
2. Evaluate
   Every individual in the population evaluated using objective functions or fitness functions. This fitness function measures how well that solution works in solving the problem. A better solution would have higher fitness value, whereas a worse solution would have lower fitness value.
3. Selection
   After all the individuals in the population are evaluated, the selection process is done to select individuals who will survive and contribute to the next generation. This selection is based on fitness value, where individuals with higher fitness value have greater opportunities to choose from.
4. Crossover (Recombination)
   Once the best individuals are selected, they will be combined or "mated" to produce new individuals. This process is called crossover or recombination, where genes of two parent individuals are exchanged to produce offspring that may have better combination genes.
5. Mutation
   After the crossovers, the mutation process is done on some individuals. This mutation aims to introduce new variations into the population, so that algorithms don't get stuck on local solutions. Mutations are done by changing the value of one or some genes in random individuals.
6. Reevaluation and Iteration
   A new population formed from the process of selections, crossovers, and mutations then reevaluated using fitness functions, and this cycle is repeated for generations until the cessation is achieved, for example after a certain iteration or after it is found an adequate solution.

Genetics algorithm application in Global Optimization

Genetic algorithms are very useful for problems. global optimization, which is the optimal solution search in a very large and complex solution space. Some areas where genetic algorithms are often applied are:

1. Portolio optimization in Finance
   Genetic algorithms can be used to optimize investment portfolio by selecting a combination of assets that provide maximum advantage at minimal risk. In this problem, each individual represents a different portfolio, and fitness is measured by the return rate and risk of the portfolio.
2. Optimize Design in Engineering
   In engineering, genetic algorithms are often used to search for the optimal design of a product, such as machine design, structure, or electronic system. By variing design parameters, genetic algorithms can find a combination of parameters that produce the best performance.
3. Scheduling and Planning
   Genetic algorithms are applied a lot in scheduling and planning problems, such as scheduling production in factories or allocation of resources. Every individual in the population represents a possible schedule, and genetic algorithm helps find the best schedule that minimizes completion time or cost.
4. Modeling and Sims in Biology
   In computing biology, genetic algorithms are used to model the evolution of organisms or other biological systems. For example, genetic algorithms can be used to simulate organism adaptations to certain environments through artificial natural selection.
5. Solutions Search in Artificial Intelligence
   In artificial intelligence, genetic algorithms can be used to train agents or systems that aim to complete specific tasks. For example, genetic algorithms can be used in robotic optimizations to find the most efficient motion configuration.

Genetic Algorithm Master

Genetic algorithms have some advantages that make them superior in global optimization:

1. Search Abilities in the Various Solutions Room
   Genetic algorithms are capable of exploring huge space solutions, which are difficult to reach by conventional search methods. By using the principles of selection and mutation, these algorithms were able to avoid a local solution trap and find better global solutions.
2. The ability to handle Non- Linear issues
   Genetic algorithms don't require a linear assumption in a solved problem. This makes it very suitable for complex and nonlinear problems, which cannot be solved with traditional optimizations methods such as linear programming.
3. Flexibility
   Genetic algorithms can be used for all kinds of problems, from decision-making, engineering design to biological simulations. This flexibility makes him popular in various disciplines.

Challenge and limitations

Despite having many advantages, genetic algorithms also have some challenges and limitations:

1. Old computation time
   Genetic algorithms take a relatively long time of computing, especially for a very complex problem that involves a lot of variables. This is due to the iteration process that must be done repeatedly until optimal solutions are found.
2. Exact Parameters Selection
   The effectiveness of genetic algorithms depends heavily on parameter selection, such as population size, mutation rate, and crossover probability. If these parameters are not selected properly, algorithms can become less efficient.
3. Potential for overfitting
   In some cases, genetic algorithms can produce too specific solutions to specific data sets, so they don't generalize well into the broader data set.

Conclusion

Genetic algorithms are one of the most effective innovative approaches to solving global optimization problems. By imitating the process of natural evolution, it is able to explore vast space solutions and find solutions that are approaching optimal. However, in order to get the best results, the genetic algorithm has to be designed and adjusted to the problems it faces, including the correct parameter selection and evaluation of the appropriate fitness function. In this modern era, with the advancement of computing technology, the application of genetic algorithms will continue to evolve and play a crucial role in many areas.

Source: Goldberg, D.E. Genetic algorithms in Search, Optimization, and Machine Learning. Addison-Wesley.