Genetic algorithms

HL CONTENT: Modeling & Simulation

The genetic algorithm is a method for solving both constrained and unconstrained optimization problems that is based on natural selection, the process that drives biological evolution. The genetic algorithm repeatedly modifies a population of individual solutions. At each step, the genetic algorithm selects individuals at random from the current population to be parents and uses them to produce the children for the next generation. Over successive generations, the population "evolves" toward an optimal solution. You can apply the genetic algorithm to solve a variety of optimization problems that are not well suited for standard optimization algorithms, including problems in which the objective function is discontinuous, nondifferentiable, stochastic, or highly nonlinear. The genetic algorithm can address problems of mixed integer programming, where some components are restricted to be integer-valued.

Use of genetic algorithms

• Click here for a fairly good list
• Click here for several good examples (scroll down)
• here are some short examples of GA

The basic pattern of genetic algorithms

1. A random set of solutions would be generated on the sample documents
2. And tested against the human labelling
3. Best fit solutions retained
4. New generation created by mutating/crossing
5. Algorithm repeated
6. Until a good fit obtained

Terms associated with genetic algorithms

• Brute force approach
• Combinatorial optimization
• Computational intractability
• Convergence
• Crossover / crossover operator
• Elitism
• Exploration vs exploitation
• Fitness / fitness function / fitness landscape
• Heuristics
• Hill climbing
• Initialization parameters
• Local extrema
• Mating pool
• Mutation / mutation rate
• Novelty search
• Offspring
• Optimization
• Population
• Premature convergence
• Problem space
• Ranking
• Roulette wheel selection
• Selection strategy
• Simulated annealing
• Stochastic universal sampling
• Termination condition
• Tour
• Tournament selection
• Truncation selection

Helpful resources

• This article is a good place to get started
• a good game: https://andymakes.itch.io/combat-genetics
• a better game to help you understand genetic algorithms: https://david-birge-cotte.itch.io/evolution-sandbox

Standards

• Outline the use of genetic algorithms.

Use an idea, equation, principle, theory or law in relation to a given problem or issue.

A natural number, a negative of a natural number, or zero.

Apply knowledge or rules to put theory into practice.

Give a brief account.