• Genetic Algorithms;
• Classifier Systems.

It has been engineered in order to be used into Swarm or others agent based simulation's models, to easy obtain "minded" agents who are fully autonomous, able to decide their own behaviors and able to change it to fit in different environmental conditions.Another main usage of the algorithm is to search bounded optimal solutions in very wide solution spaces and for quite undefined problems. This kind of problems are solved using the convergence method: the best result is assumed to be achieved when a given convergence of the same solution exist in the population. It is widely accepted as mathematical proof that the genetic algorithm, due to its fitness-proportionate reproduction, converges to better solutions.

The genetic algorithm's implementation, starting from John Holland's work, introduces some extensions and innovations:

1. extended alphabet: each gene can be represented by up to 32000 values. In a standard representation the genes have a binary alphabet and so the genomes have to be explicitly translated into the various aspects composing the solution, which after some manipulation, as crossover or mutation, can become meaningless. With the extended alphabet each allele can be a meaningful part of the solution and the translation process is easier.
2. multi genome: each individual of the population is represented by a chromosome that could be composed by a variable number of genomes. Each genome of a chromosome represent a "substrategy" and the chromosome is the genetic algorithm's formalism for a "strategy" driving the actions of the simulated agent. The multi genome schema give a high degree of freedom to the user in formalizing problems in which coexist different binded aspects.
3. rescale fitness operator: the natural selection process has been modified in order to improve efficiency and manage negative fitness values. The technique utilized consist in rescale the fitness of all the chromosome.
4. univocal genome: using this option each value of the alphabet is unique whitin the genome, it means that in a genome there can not be two or more identical genes.