Alternatives to Darwinian evolutionAlternatives to Darwinian evolution have been proposed by scholars investigating biology to explain signs of evolution and the relatedness of different groups of living things. The alternatives in question do not deny that evolutionary changes over time are the origin of the diversity of life, nor that the organisms alive today share a common ancestor from the distant past (or ancestors, in some proposals); rather, they propose alternative mechanisms of evolutionary change over time, arguing against mutations acted on by natural selection as the most important driver of evolutionary change.
Unit of selectionA unit of selection is a biological entity within the hierarchy of biological organization (for example, an entity such as: a self-replicating molecule, a gene, a cell, an organism, a group, or a species) that is subject to natural selection. There is debate among evolutionary biologists about the extent to which evolution has been shaped by selective pressures acting at these different levels. There is debate over the relative importance of the units themselves.
Evolutionary programmingEvolutionary programming is one of the four major evolutionary algorithm paradigms. It is similar to genetic programming, but the structure of the program to be optimized is fixed, while its numerical parameters are allowed to evolve. It was first used by Lawrence J. Fogel in the US in 1960 in order to use simulated evolution as a learning process aiming to generate artificial intelligence. Fogel used finite-state machines as predictors and evolved them.
Gene-centered view of evolutionThe gene-centered view of evolution, gene's eye view, gene selection theory, or selfish gene theory holds that adaptive evolution occurs through the differential survival of competing genes, increasing the allele frequency of those alleles whose phenotypic trait effects successfully promote their own propagation. The proponents of this viewpoint argue that, since heritable information is passed from generation to generation almost exclusively by DNA, natural selection and evolution are best considered from the perspective of genes.
Artificial immune systemIn artificial intelligence, artificial immune systems (AIS) are a class of computationally intelligent, rule-based machine learning systems inspired by the principles and processes of the vertebrate immune system. The algorithms are typically modeled after the immune system's characteristics of learning and memory for use in problem-solving. The field of artificial immune systems (AIS) is concerned with abstracting the structure and function of the immune system to computational systems, and investigating the application of these systems towards solving computational problems from mathematics, engineering, and information technology.
Facilitated variationThe theory of facilitated variation demonstrates how seemingly complex biological systems can arise through a limited number of regulatory genetic changes, through the differential re-use of pre-existing developmental components. The theory was presented in 2005 by Marc W. Kirschner (a professor and chair at the Department of Systems Biology, Harvard Medical School) and John C. Gerhart (a professor at the Graduate School, University of California, Berkeley).
