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Target function

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

A target function is a mathematical function that an evolutionary program is intended to reproduce or approximate.

A target function is the mathematical relationship an evolutionary program aims to reproduce or approximate. In gene expression programming, evolution searches for an expression whose outputs match this function. section 12.1

Changing the target

Replace the simple linear target with a more complex equation, rerun evolution, and observe how the resulting expression and fitness change. section 4.1.2

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

How deep it goes
Defines a linear target, implements its fitness comparison, and exercises changes in complexity without deriving approximation theory.
Examples
Uses f(x1) = 6x1 + 22, sample-input predictions, mean absolute error, and a simplified evolved equation.
What it assumes
Assumes familiarity with evolutionary computation and Python notebooks while demonstrating target definition and evaluation concretely.
What you can do after
Build it from scratch with Geppy by defining a target, evolving candidate expressions, measuring error, and inspecting the best result.
How it connects
Connects target functions with gene expression programming, fitness functions, expression trees, and genetic operators.
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.