Core vocabulary of generative systems, autonomous processes, and emergent complexity.
Agent
An autonomous entity that perceives its environment and takes actions to achieve goals. In generative systems, agents follow local rules that produce global patterns through interaction.
Attractor
A set of states toward which a dynamical system tends to evolve. Point attractors are fixed states; limit cycles are periodic; strange attractors produce chaotic, non-repeating orbits.
Autonomous System
A system capable of operating independently without human intervention, governed by internal rules that determine its behavior in response to environmental inputs.
Cellular Automata
Discrete models where a grid of cells evolves through time according to rules based on neighboring cell states. Conway's Game of Life is the canonical example of emergent complexity.
Complexity
The property of systems whose behavior cannot be easily predicted from their components. Complex systems exhibit emergent phenomena, sensitivity to initial conditions, and adaptive responses.
Convergence
The tendency of an iterative process to approach a stable state or solution. In evolutionary algorithms, populations converge toward high-fitness solutions over successive generations.
Emergence
The arising of complex patterns, structures, or behaviors from the interaction of simpler components. The whole exhibits properties that none of its parts possess individually.
Entropy
A measure of disorder or uncertainty in a system. In information theory, entropy quantifies the average information content of a message. Generative systems often explore the tension between order and entropy.
Evolution
Change in heritable characteristics of populations over successive generations. Computational evolution uses selection, crossover, and mutation to optimize solutions to complex problems.
Feedback
The process where system outputs are routed back as inputs, creating loops that can amplify (positive) or dampen (negative) changes. Feedback is fundamental to self-regulation and chaos.
Fitness Function
A measure of how well a candidate solution solves a given problem in evolutionary computation. The fitness function guides selection pressure toward increasingly capable organisms.
Generative
Describing a process, system, or artwork produced by an autonomous set of rules rather than direct authorship. The artist defines the system; the system creates the work.
Genetic Algorithm
An optimization technique inspired by natural selection. Populations of candidate solutions are evolved through selection of fittest members, crossover of genetic material, and random mutation.
L-System
A parallel rewriting system used to model plant growth and fractal structures. Starting from an axiom, production rules repeatedly expand symbols, generating complex branching geometries.
Mutation
Random alteration of genetic information. In evolutionary algorithms, mutation introduces variation that prevents premature convergence and enables exploration of novel solution spaces.
Noise Field
A spatially coherent random function (Perlin, Simplex) that varies smoothly across dimensions. Used to drive particle flow, texture generation, and organic-feeling procedural motion.
Phase Space
An abstract space where each possible state of a dynamical system is represented as a unique point. Trajectories through phase space reveal the long-term behavior of complex systems.
Reaction-Diffusion
A mathematical model describing how two chemical species interact and spread. The Gray-Scott model produces spots, stripes, and labyrinthine patterns seen in animal markings and coral growth.
Rule
A deterministic or probabilistic instruction that governs system behavior. Generative art derives its power from the gap between simple rules and the complexity of their collective output.
Self-Organization
The process by which global order arises spontaneously from local interactions without central coordination. Examples: ant colonies, neural synchronization, market dynamics.
Strange Attractor
A fractal structure in phase space toward which chaotic systems evolve. Trajectories never repeat yet stay bounded — infinite variation within finite form. Lorenz and Rössler are classic examples.
Swarm
A large number of simple agents exhibiting collective intelligence through local interaction. Boids flocking, ant foraging, and murmuration demonstrate how swarms solve complex problems without leaders.