An evolutionary algorithm is an evolutionary AI based computer application that solves problems by employing processes that mimic the behaviors of living things. As such, it uses mechanisms that are typically associated with biological evolution, such as reproduction, mutation and recombination.
Evolutionary algorithms function in a Darwinian-like natural selection process; the weakest solutions are eliminated while stronger, more viable options are retained and re-evaluated in the next evolution—with the goal being to arrive at optimal actions to achieve the desired outcomes.
What are the business benefits of evolutionary algorithms?
Multiple business benefits are associated with evolutionary algorithms, including:
Increased flexibility. Evolutionary algorithm concepts can be modified and adapted to solve the most complex problems humans face and meet target objectives.
Better optimization. The vast “population” of all possible solutions is taken into consideration. This means the algorithm is not restricted to a particular solution.
Unlimited solutions. Unlike classical methods that present and attempt to maintain a single best solution, evolutionary algorithms include and can present multiple potential solutions to a problem.
What are the stages of evolutionary algorithms?
1.Initialization: An initial population of candidate solutions is generated randomly or using some heuristic method.
2.Evaluation: Each candidate solution in the population is evaluated according to some predefined fitness function that measures how well it solves the optimization problem.
3.Selection: Individuals in the population are selected to serve as parents for the next generation based on their fitness. Selection methods include roulette wheel selection, tournament selection, and rank-based selection.
4.Recombination (Crossover): Pairs of selected individuals (parents) are combined to produce offspring using crossover operators. Crossover involves exchanging parts of the parent solutions to create new solutions.
5.Mutation: Mutation introduces random changes to the offspring solutions to maintain genetic diversity within the population. Mutation can involve small changes to individual genes or larger structural changes to the solutions.
6.Replacement: The offspring population replaces the previous population to form the next generation. Replacement strategies vary and can include methods like generational replacement or steady-state replacement.
7.Termination: The algorithm terminates when a stopping criterion is met, such as reaching a maximum number of generations, achieving a satisfactory level of fitness, or running out of computational resources.
Benefits of Evolutionary Algorithms:
Suitable for complex problems: EAs can handle problems with many variables, complex search spaces, and non-linear relationships between variables, making them suitable for situations where other optimization methods might struggle.
Robustness: EAs are less prone to getting stuck in local optima compared to some traditional optimization methods.
Flexibility: EAs can be adapted to various problem domains by modifying the representation of individuals and the fitness function.
Evolutionary algorithms come in various flavors, including Genetic Algorithms (GA), Genetic Programming (GP), Evolution Strategies (ES), and Differential Evolution (DE), among others. They have been applied successfully to a wide range of optimization problems in fields such as engineering, economics, finance, and biology.