External links
- Framsticks home page
- Worlds and Organisms sample gallery
- Comparison of Different Encodings for Simulated 3D Agents
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| Developer(s) | Maciej Komosinski and Szymon Ulatowski |
|---|---|
| Stable release | 5.0 / July 14, 2024 |
| Operating system | Microsoft Windows, Linux, Mac OS X, iOS, Android |
| Website | www |
Framsticks is a 3D freeware Artificial Life simulator. Organisms consisting of physical structures ("bodies") and control structures ("brains") evolve over time against a user's predefined fitness landscape (for instance, evolving for speed), or spontaneously coevolve in a complex environment. Evolution of organisms occurs primarily through artificial selection, where an intelligent selector chooses the selection parameters and mutation rates. Also the organisms rate of crossing-over can be chosen thus reflecting the sharing of genes by mating in nature. The simulated organisms have genetic scripts inspired by DNA found in living organisms in nature. A user can isolate a particular organism in the gene pool and edit its genotype. Framsticks allows users to design organisms or manually edit the living genetic code of an organism. Users have the ability to seed the environment with energy orbs that the organisms convert to energy and material. Depending on how the organism does in its lifespan determines the future of the virtual gene pool. Gene pools can be exported and shared.
The bodies are made up of various building blocks that are assembled according to a genetic script. Building blocks include: a rotator, hinge, muscle, structure, and receptor.
The brains are basic neural networks that show up as a network of firing neurons. The genetic script serves as the blueprints for the exact assembly and functioning of the neural network.
The world or ‘universe’ can be set to height-field editable as blocks and/or steep planes, ‘water’, flat, or a combination of all these and be edited by user as map in simple text-format. It has adjustable gravitation and water-level.
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Biology – The natural science that studies life. Areas of focus include structure, function, growth, origin, evolution, distribution, and taxonomy.
In computer science and operations research, a genetic algorithm (GA) is a metaheuristic inspired by the process of natural selection that belongs to the larger class of evolutionary algorithms (EA). Genetic algorithms are commonly used to generate high-quality solutions to optimization and search problems by relying on biologically inspired operators such as mutation, crossover and selection. Some examples of GA applications include optimizing decision trees for better performance, solving sudoku puzzles, hyperparameter optimization, causal inference, etc.
In computer science, evolutionary computation is a family of algorithms for global optimization inspired by biological evolution, and the subfield of artificial intelligence and soft computing studying these algorithms. In technical terms, they are a family of population-based trial and error problem solvers with a metaheuristic or stochastic optimization character.
NeuroEvolution of Augmenting Topologies (NEAT) is a genetic algorithm (GA) for the generation of evolving artificial neural networks developed by Kenneth Stanley and Risto Miikkulainen in 2002 while at The University of Texas at Austin. It alters both the weighting parameters and structures of networks, attempting to find a balance between the fitness of evolved solutions and their diversity. It is based on applying three key techniques: tracking genes with history markers to allow crossover among topologies, applying speciation to preserve innovations, and developing topologies incrementally from simple initial structures ("complexifying").
A generegulatory network (GRN) is a collection of molecular regulators that interact with each other and with other substances in the cell to govern the gene expression levels of mRNA and proteins which, in turn, determine the function of the cell. GRN also play a central role in morphogenesis, the creation of body structures, which in turn is central to evolutionary developmental biology (evo-devo).
Bio-inspired computing, short for biologically inspired computing, is a field of study which seeks to solve computer science problems using models of biology. It relates to connectionism, social behavior, and emergence. Within computer science, bio-inspired computing relates to artificial intelligence and machine learning. Bio-inspired computing is a major subset of natural computation.
Evolutionary biology is the subfield of biology that studies the evolutionary processes that produced the diversity of life on Earth. It is also defined as the study of the history of life forms on Earth. Evolution holds that all species are related and gradually change over generations. In a population, the genetic variations affect the phenotypes of an organism. These changes in the phenotypes will be an advantage to some organisms, which will then be passed on to their offspring. Some examples of evolution in species over many generations are the peppered moth and flightless birds. In the 1930s, the discipline of evolutionary biology emerged through what Julian Huxley called the modern synthesis of understanding, from previously unrelated fields of biological research, such as genetics and ecology, systematics, and paleontology.
Neuroevolution, or neuro-evolution, is a form of artificial intelligence that uses evolutionary algorithms to generate artificial neural networks (ANN), parameters, and rules. It is most commonly applied in artificial life, general game playing and evolutionary robotics. The main benefit is that neuroevolution can be applied more widely than supervised learning algorithms, which require a syllabus of correct input-output pairs. In contrast, neuroevolution requires only a measure of a network's performance at a task. For example, the outcome of a game can be easily measured without providing labeled examples of desired strategies. Neuroevolution is commonly used as part of the reinforcement learning paradigm, and it can be contrasted with conventional deep learning techniques that use backpropagation with a fixed topology.
Life simulation games form a subgenre of simulation video games in which the player lives or controls one or more virtual characters. Such a game can revolve around "individuals and relationships, or it could be a simulation of an ecosystem". Other terms include artificial life game and simulated life game (SLG).
Humans have considered and tried to create non-biological life for at least 3,000 years. As seen in tales ranging from Pygmalion to Frankenstein, humanity has long been intrigued by the concept of artificial life.
Computational neurogenetic modeling (CNGM) is concerned with the study and development of dynamic neuronal models for modeling brain functions with respect to genes and dynamic interactions between genes. These include neural network models and their integration with gene network models. This area brings together knowledge from various scientific disciplines, such as computer and information science, neuroscience and cognitive science, genetics and molecular biology, as well as engineering.
Neurorobotics is the combined study of neuroscience, robotics, and artificial intelligence. It is the science and technology of embodied autonomous neural systems. Neural systems include brain-inspired algorithms, computational models of biological neural networks and actual biological systems. Such neural systems can be embodied in machines with mechanic or any other forms of physical actuation. This includes robots, prosthetic or wearable systems but also, at smaller scale, micro-machines and, at the larger scales, furniture and infrastructures.
Natural computing, also called natural computation, is a terminology introduced to encompass three classes of methods: 1) those that take inspiration from nature for the development of novel problem-solving techniques; 2) those that are based on the use of computers to synthesize natural phenomena; and 3) those that employ natural materials to compute. The main fields of research that compose these three branches are artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, fractal geometry, artificial life, DNA computing, and quantum computing, among others.
Evolutionary biology, in particular the understanding of how organisms evolve through natural selection, is an area of science with many practical applications. Creationists often claim that the theory of evolution lacks any practical applications; however, this claim has been refuted by scientists.
In evolutionary biology, robustness of a biological system is the persistence of a certain characteristic or trait in a system under perturbations or conditions of uncertainty. Robustness in development is known as canalization. According to the kind of perturbation involved, robustness can be classified as mutational, environmental, recombinational, or behavioral robustness etc. Robustness is achieved through the combination of many genetic and molecular mechanisms and can evolve by either direct or indirect selection. Several model systems have been developed to experimentally study robustness and its evolutionary consequences.
The theoretical foundations of evolutionary psychology are the general and specific scientific theories that explain the ultimate origins of psychological traits in terms of evolution. These theories originated with Charles Darwin's work, including his speculations about the evolutionary origins of social instincts in humans. Modern evolutionary psychology, however, is possible only because of advances in evolutionary theory in the 20th century.
3D Virtual Creature Evolution, abbreviated to 3DVCE, is an artificial evolution simulation program created by Lee Graham. Its purpose is to visualize and research common themes in body plans and strategies to achieve a fitness function of the artificial organisms generated and maintained by the system in their given environment. The program was inspired by Karl Sims’ 1994 artificial evolution program, Evolved Virtual Creatures. The program is run through volunteers who download the program from the home website and return information from completed simulations. As of March 4, 2013, it is available to download on Windows, MacOS, and in some cases Linux.
Artificial life is a field of study wherein researchers examine systems related to natural life, its processes, and its evolution, through the use of simulations with computer models, robotics, and biochemistry. The discipline was named by Christopher Langton, an American theoretical biologist, in 1986. In 1987, Langton organized the first conference on the field, in Los Alamos, New Mexico. There are three main kinds of alife, named for their approaches: soft, from software; hard, from hardware; and wet, from biochemistry. Artificial life researchers study traditional biology by trying to recreate aspects of biological phenomena.
Evolving digital ecological networks are webs of interacting, self-replicating, and evolving computer programs that experience the same major ecological interactions as biological organisms. Despite being computational, these programs evolve quickly in an open-ended way, and starting from only one or two ancestral organisms, the formation of ecological networks can be observed in real-time by tracking interactions between the constantly evolving organism phenotypes. These phenotypes may be defined by combinations of logical computations that digital organisms perform and by expressed behaviors that have evolved. The types and outcomes of interactions between phenotypes are determined by task overlap for logic-defined phenotypes and by responses to encounters in the case of behavioral phenotypes. Biologists use these evolving networks to study active and fundamental topics within evolutionary ecology.
This glossary of genetics and evolutionary biology is a list of definitions of terms and concepts used in the study of genetics and evolutionary biology, as well as sub-disciplines and related fields, with an emphasis on classical genetics, quantitative genetics, population biology, phylogenetics, speciation, and systematics. Overlapping and related terms can be found in Glossary of cellular and molecular biology, Glossary of ecology, and Glossary of biology.