Human-based genetic algorithm

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:

system sequences innovator selector
natural selection nucleotide nature nature
artificial selection nucleotide nature human
genetic engineering nucleotide human human
human-based genetic algorithm data human human
interactive genetic algorithm data computer human
genetic algorithm data computer computer

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

Functional features

Applications

The HBGA methodology was derived in 1999-2000 from analysis of the Free Knowledge Exchange project that was launched in the summer of 1998, in Russia (Kosorukoff, 1999). Human innovation and evaluation were used in support of collaborative problem solving. Users were also free to choose the next genetic operation to perform. Currently, several other projects implement the same model, the most popular being Yahoo! Answers, launched in December 2005.

Recent research suggests that human-based innovation operators are advantageous not only where it is hard to design an efficient computational mutation and/or crossover (e.g. when evolving solutions in natural language), but also in the case where good computational innovation operators are readily available, e.g. when evolving an abstract picture or colors (Cheng and Kosorukoff, 2004). In the latter case, human and computational innovation can complement each other, producing cooperative results and improving general user experience by ensuring that spontaneous creativity of users will not be lost.

Furthermore, human-based genetic algorithms prove to be a successful measure to counteract fatigue effects introduced by interactive genetic algorithms.[1]

See also

References

  1. ↑ Kruse, J.; Connor, A. (2015). "Multi-agent evolutionary systems for the generation of complex virtual worlds". EAI Endorsed Transactions on Creative Technologies 15 (5). doi:10.4108/eai.20-10-2015.150099.

External links

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