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Queen's gambit

As explained in Evolutionary Deep Learning: Genetic algorithms and neural networks

The Queen's Gambit is a chessboard placement problem that asks for a specified number of queens to be placed so that none can capture another.

The Queen's Gambit is a chessboard placement problem: place n queens on an n-by-n board so that no queen can capture another. Its unrestricted search space makes it a practical demonstration of genetic algorithms and their controlled randomness. section 3.2

A DEAP implementation represents candidates as permutations, evaluates diagonal conflicts as fitness, and evolves a population through partially matched crossover, shuffled-index mutation, and tournament selection. A zero-conflict or target fitness ends the search.

How Evolutionary Deep Learning: Genetic algorithms and neural networks covers it

How deep it goes
Provides runnable board plotting, fitness evaluation, DEAP configuration, and evolution parameters, but not theoretical proof.
Examples
Uses an eight-by-eight board, random queen placements, diagonal-conflict counts, and a plotted evolved solution.
What it assumes
Assumes basic Python and chess movement while teaching the problem encoding, fitness measure, and genetic workflow through executable code.
What you can do after
Apply the DEAP toolbox to configure, evolve, visualize, and inspect solutions to the queen placement problem.
How it connects
Connects chessboard placement with genetic algorithms, evolutionary algorithms, fitness functions, crossover, mutation, selection, and DEAP.
Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks
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This explainer was generated by Manning's knowledge graph from the text of Evolutionary Deep Learning: Genetic algorithms and neural networks. The section links point to the parts of the book it draws on.