Montréal (Québec) Canada
Produced with the help of
Le Ministère des Affaires Culturelles du Québec (Recherche-Innovation program)
Additional programming by
LifeTools is a set of MaxMSP objects based on some Artificial Life basic algorithm functions. The Mutation and Crossover objects from LifeTools are inspired by the Mutate and Crossover objects written by Gary Lee Nelson (included in the old Rand external). However, instead of manipulating bit strings, LifeTools objects process Lists of integers. All objects run on MacOS9, MacOSX and Windows versions of MaxMSP.
CA is a one dimensional cellular automaton. It is modeled as a linear row of cells where each cell has one of two possible states: alive or dead. The rules are based on proximity as each cell has two neighbors: left or right. For each generation, a cell sets its state anew according to predefined proximity rules. These rules can be set by selecting the CA object and choosing Get Info in the Max menu.
Life / Life2x / LifeTorus
is a cellular automaton based on Conway's Game of Life. It is modeled
as a 2D grid of cells and each cell has one of two possible states: alive
or dead. The rules are based on proximity as each cell has eight neighbors:
left, right, above, below, and on four corners. For each generation, a
cell sets its state anew according to the following two rules:
Life3D / LifeGL
the Life object, this cellular automaton is based on Conway's Game of
Life. It is modeled as a 3D grid of cells and each cell has one of two
possible states: alive or dead. The rules are based on proximity as each
cell has 26 neighbors. For each generation, a cell sets its state anew
according to the same two rules as the 2D Game of Life:
The Mutation object mutates a list of numbers based on random probabilities. This object offers 5 different mutation modes.
The Crossover object performs a crossover between two lists of integers to create 2 new children Lists. Each parent List is divided into n size segments. According to the crossover probability of each segment, the Crossover function swaps (or not) this segment between Parent A and Parent B.
Evolve is based on conventional genetic algorithm. It produces offsprings (new Lists) from an initial List of integers. The user selects which offspring he prefers (the fitness function is not part of the object) and new offsprings are generated using reproduction, mutation and crossover.
Bill Vorn 2014