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Particle operator

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

A particle operator is a procedure that generates, updates, or evaluates particles in particle swarm optimization.

A particle operator is a procedure that generates, updates, or evaluates particles during particle swarm optimization. Together, these operations create candidate solutions, move them toward promising particles, and measure fitness. section 4.2.1

Finding cannon-shot parameters

Particles represent guesses for launch velocity and angle. Repeated evaluation and updating swarm these guesses toward parameters that reach the selected target distance, after which the best particle is converted back into a distance. section 4.2.1

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

How deep it goes
Defines operators and traces their Python mechanics, stopping short of formal convergence analysis.
Examples
Uses launch velocity, shot angle, target distance, swarm scatter plots, and a 100-generation optimization loop.
What it assumes
Assumes familiarity with DEAP toolbox setup and basic particle swarm optimization terminology.
What you can do after
Apply it via DEAP by registering, executing, and inspecting particle generation, updating, evaluation, and best-solution output.
How it connects
Connects particle swarm optimization with fitness evaluation, velocity update, personal best, and global best.
Cover of Evolutionary Deep Learning: Genetic algorithms and neural networks
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Related concepts

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.