In evolutionary computation, a human-based genetic algorithm (HBGA) is a genetic algorithm that allows humans to contribute solution suggestions to the evolutionary process. For this purpose, a HBGA has human interfaces for initialization, mutation, and recombinant crossover. As well, it may have interfaces for selective evaluation. In short, a HBGA outsources the operations of a typical genetic algorithm to humans. Evolutionary genetic systems and human agency Among evolutionary genetic systems, HBGA is the computer-based analogue of genetic engineering (Allan, 2005). This table compares systems on lines of human agency: One obvious pattern in the table is the division between organic (top) and computer systems (bottom). Another is the vertical symmetry between autonomous systems (top and bottom) and human-interactive systems (middle). Looking to the right, the selector is the agent that decides fitness in the system. It determines which variations will reproduce and contribute to the next generation. In natural populations, and in genetic algorithms, these decisions are automatic; whereas in typical HBGA systems, they are made by people. The innovator is the agent of genetic change. The innovator mutates and recombines the genetic material, to produce the variations on which the selector operates. In most organic and computer-based systems (top and bottom), innovation is automatic, operating without human intervention. In HBGA, the innovators are people. HBGA is roughly similar to genetic engineering. In both systems, the innovators and selectors are people. The main difference lies in the genetic material they work with: electronic data vs. polynucleotide sequences. Differences from a plain genetic algorithm All four genetic operators (initialization, mutation, crossover, and selection) can be delegated to humans using appropriate interfaces (Kosorukoff, 2001). Initialization is treated as an operator, rather than a phase of the algorithm. This allows a HBGA to start with an empty population. Initialization, mutation, and crossover operators form the group of innovation operators. Choice of genetic operator may be delegated to humans as well, so they are not forced to perform a particular operation at any given moment. Functional features HBGA is a method of collaboration and knowledge exchange. It merges competence of its human users creating a kind of symbiotic human-machine intelligence (see also distributed artificial intelligence). Human innovation is facilitated by sampling solutions from population, associating and presenting them in different combinations to a user (see creativity techniques). HBGA facilitates consensus and decision making by integrating individual preferences of its users. HBGA makes use of