The Genetic Algorithms & Programming Component Library (GACL) is a powerful genetic algorithms and genetic programming solution for Delphi and Appmethod Win32, Win64, OSX, iOS, Android, and Linux! Designed for Delphi 2010-Tokyo (Win32/Win64/OSX/iOS/Android/Linux) and Appmethod (Object Pascal), the GACL provides simple yet powerful components for designing, evolving, and using genetic algorithms and genetic programs. The Genetic Algorithms & Programming Component Library is now in version 6.x. Version 6.x is a free upgrade for users who bought the GACL after Dec 1st, 2014. Users who bought the GACL before December 1st, 2014 need to purchase an upgrade from the orders page.

Genetic Algorithms and Genetic Programming help you automatically solve a wide range of problems, from optimization and search problems using genetic algorithms to data fitting, prediction and modelling, or decision strategy and game control using genetic programming.

Key Benefits
-100% Source Code
-For Vcl and FMX
-For Win32, Win64, OSX, iOS, Android, and Linux
-Integrated IDE Help Insight as well as Help File and Online Documentation
-Unlimited Population
-Read and write your genetic solutions or progress as XML
-6 selection methods (Roulette, Random, Tournament, Stochastic Tournament, Elitist, or Custom)
-4 Fitness Search Methods (Minimize, Maximize, Weighted Minimize, and Weighted Maximize)
-Multi-threaded evolve and fitness evaluation available for XE7+
Genetic Algorithms
-Unlimited Chromosome size
-Helper “gene” class to read and write arbitrarily-sized integers, enumerations, and floating point numbers into the bits of the chromosome
-3 Genetic Operations (crossover, mutation, and inversion)
-5 Crossover Methods (gene boundary, bit boundary, byte boundary, word boundary, and long word boundary)
-Genetic Algorithms XML Schema for saving and loading genetic algorithms problems and solutions
Genetic Programming
-Specify your problem more intuitively using functions, constants, and variables
-Easily add "personality" to your games and programs by evolving different solutions to the same problem (e.g., a cautious AI, a daring AI, etc)
-Completely rebuilt from the ground up Tree-based Genetic Programming with Functions, Constants, and Variables
-Generics-based Genetic Programming implementation
-6 Initialization Methods (Full, Grow, Half and Half, Ramped Full, Ramped Grow, and Ramped Half and Half)
-3 Basic Genetic Operations (crossover, mutation, and inversion)
-6 Different Mutation Methods (Subtree, Replacement, Constant, Shrink, Hoist, and Point)
-17 Different Bloat Control Strategies (Limit Size or Depth, Tarpeian Size or Depth, Unfit Size or Depth, Shrink Size or Depth, Hoist Size or Depth, Size Fair or Depth Fair Crossover, Size or Depth Parsimony Pressure, Covariant Size or Depth Parsimony Pressure, and Lexicographic Parsimony Pressure)
-Executor component for executing your winning genetic programs
-Genetic Programming XML Schema for saving and loading genetic programming problems and solutions
See the GACL Version History page for full details on what has changed.

For Delphi 2010-Tokyo (Win32/Win64/OSX/iOSX/Android) and Appmethod (Object Pascal) (Earlier versions are available for Delphi 2009 and earlier)

Genetic algorithms (GA) are computer science techniques that seek to solve optimization or search problems. They are inspired by evolutionary biology and approach the search problem as a task of evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).

Genetic algorithms have been used in bioinformatics, phylogenetics, computational science, engineering, economics, chemistry, manufacturing, mathematics, physics, pharmacometrics and other fields.

Genetic Programming
Genetic programming (GP) is a computer science method, inspired by evolutionary biology, for automatically solving problems, without having to know or define the form or structure of optimum problem structure beforehand. You define the basic building blocks (functions, constants, and variables) of the problem and then the component does the rest. Genetic programming solves problems by evolving a group or population of candidate individuals through successive generations, selecting fitter (or better) child individuals for each generation, until a solution is found. It uses evolutionary biology techniques such as inheritance, mutation, selection, and crossover (also called recombination).

Genetic Programming is a specialization of genetic algorithms where each individual is a computer program. It has found success as a automatic programming tool, a machine learning tool or an automatic problem-solving engine. Genetic programming can be used for Curve Fitting, Data Modelling and Symbolic Regression; Decision Strategy, Game Control, and Industrial Process Control; Image and Signal Processing; and Financial Trading, Time Series Prediction and Economic Modelling.

Only for V.I.P