Python Genetic Algorithms With Artificial Intelligence

What are Genetic Algorithms With Python?

A Genetic Algorithm (GA) is a metaheuristic inspired by natural selection and is a part of the class of Evolutionary Algorithms (EA). We use these to generate high-quality solutions to optimization and search problems, for which, these use bio-inspired operators like mutation, crossover, and selection. In other words, using these, we hope to achieve optimal or near-optimal solutions to difficult problems. Such algorithms simulate natural selection.

Have a look at Python Machine Learning Algorithms

For any problem, we have a pool of possible solutions. These undergo processes like recombination and mutation to bear new children over generations. The search space is the set of all possible solutions and values the inputs may take. In optimization, we try to find within this search space the point or set of points that gives us the optimal solution. Each individual is like a string of characters/integers/floats and the stringsare like chromosomes.

The fitness value (from a fitness function) for a candidate tells us how close it is to the optimal solution. This is on the lines of Darwin’s theory of ‘Survival of the Fittest’ and is how we keep producing better (evolving) individuals/ solutions over generations before reaching a criterion where to stop. These algorithms work in four steps:

1. Individuals in population compete for resources, mate
2. Fittest individuals mate to create more offsprings than others
3. Fittest parent propagates genes through generation; parents may produce offsprings better than either parent
4. Each successive generation evolves to suit its ambience

Since the population size is constant, some individuals must die to make room for newer ones. We arrive at a situation of convergence when the difference between offsprings produced by the current and ancestral populations is no longer significant. Then, the algorithm converges to a set of solutions for the problem.

Do you know about Python NLTK

Operators of Python Genetic Algorithms

To evolve through the generations, an algorithm may make use of one of many operators. Some of those are:

a. Selection Operator

It prefers individuals with better fitness scores and lets them pass genes on to successive generations.

b. Crossover Operator

This lets individuals mate. We apply the selection operator to select two individuals, and randomly choose crossover sites. We then exchange the genes at these sites- this produces an entirely new individual.

You must know about Python Ternary Operators

c. Mutation Operator

In mutation, we insert random genes in offsprings to maintain diversity and avoid premature convergence.

Python Genetic Algorithm Example

Let’s try to build a Genetic Algorithm in Python that can play something like Guess the Number better than us humans. This is a game where I randomly select a numberbetween 1 and 10 (both inclusive) and you guess what number I have picked.

Is it 7? No

Is it 3? No

Is it 6? No

Is is 2? Yes

Seems like no big deal, but when we start talking about 100 or 1000 numbers, it quickly becomes a problem. How do we improve our guesses? What can we do but depend on sheer luck? This ultimately turns into a mechanical process. Maybe we could decide if a certain guess is closer to the solution in a certain direction?

Is it 7? Lower

Is it 1? Higher

Is it 5? Lower

Is it 4? Lower

Is it 3? Yes

Possession of such domain knowledge means we can get to the solution faster. To make informed and improved guesses, the algorithms make use of random exploration of the problem space. Along with that, they also use evolutionary processes like mutation and crossover (see above). Without experience in the problem domain, they also try out things humans never would dare attempt.

Have a look at Python AI Heuristic Search

Okay, now let’s try implementing this in Python. Bear in mind that the fitness value for a candidate is how close it is to the optimal. Here, it means how many letters match what it should be- “Hello World!”. Let’s start with building a gene set.

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See Also-

• Python – Interview Questions Part 1
• Python – Interview Questions Part 2
• Python – Interview Questions Part 3
• Python – Interview Questions Part 4