µGP (MicroGP) is a versatile optimizer able to outperform both human experts and conventional heuristics in finding the optimal solution of hard problems. Given a task, it first fosters a set of random solutions, then iteratively refines and enhance them. Its heuristic algorithm uses the result of the evaluations, together with some internal structural information, to efficiently explore the search space, and eventually to produce the optimal solution. µGP has been devised in 2002 in the CAD Group at Politecnico di Torino, and subsequently developed thanks to the effort of several people.

µGP original application was the creation of assembly-language programs for testing different microprocessors, hence the Greek letter µ (micro) in its name. Afterward, it has been used on a wider range of problems, such as: creation of test programs for pre- and post-silicon validation; design of bayesian networks; creation of mathematical functions represented as trees; integer and combinatorial optimization; real-value parameter optimization; and even creation of corewar warriors.

µGP is an evolutionary algorithm, hence the GP acronym (genetic programming) in its name. A population of different solutions is considered in each step of the search process, and new individuals are generated through mechanisms that ape both sexual and asexual reproduction. New solutions inherit distinctive traits from existing ones, and may coalesce the good characteristics of different parents. Better solutions have a greater chance to reproduce and to succeed in the simulated struggle for existence.

µGP is distributed under the GNU Public License (GPL). The current major version of the toolkit is three, also known as µGP3, ugp3 (due to typographic limitations) and MicroGP++ (being written in C++). Comprehensive instructions and the updated documentation can be found online in the µGP wiki. For the practitioner, the wiki's step-by-step tutorials help understanding the gory details of configuring the tool, running it and tweaking its many parameters. For the programmer, the wiki explains the design rationale, and details the internal architecture of the toolkit.

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