Genetic Algorithms 1.3

Genetic algorithms simulation tool designed to explore search and optimisation problems, focusing on efficient convergence toward optima through evolutionary operators inspired by natural selection.

• Basic operations: reproduction, crossing, and mutation act on populations of coding sequences to search for the global optimum.
• Search control: random selection rules guide the search and are configurable via selection mechanisms and fitness-function scaling to direct the optimisation trajectory.
• Biological enhancements: additional genetic manipulations such as dominance, diploidy, inversion, and other chromosome-level reconfigurations extend the basic model.
• Custom fitness design: users can design textual or graphical scaling patterns for the fitness function and apply them in simulations.
• Visualization and analytics: the tool provides animated representations of genetic processes and preliminary statistical analysis of fitness distributions in the population.
• Experiment reproducibility: non-random procedures can be compared by using the same pseudorandom sequence across successive simulations.
• Data management: simulation results can be saved and shared for collaboration.