Evolutionary Models in Business. Evgeny Klochkov
threatening postures.
Fight against unfavorable environmental conditions. A huge number of plants are destroyed almost every year by late frosts, droughts, and sharp climatic fluctuations. The mass of seeds is carried by the wind into unfavorable conditions and perishes. Many animals die during severe winters with little snow. With a lack of oxygen in the water, fish are killed. The result of this struggle is the survival of individuals with the most favorable hereditary changes for the given living conditions. For example, desert plants have long roots and small leaves.
All of the above types of struggle for existence lead to the extermination of a huge number of individuals or to the impossibility of leaving offspring. As a result of natural selection, individuals who are most adapted to the environmental conditions in which this species lives, survive. At the same time, there are no organic forms absolutely perfectly adapted to the conditions of their life in nature. This is impossible given the variability of the environment. Having become of no use, organs that were originally formed under the influence of natural selection, can show great variability under the influence of new environmental factors. When the conditions that formed this sign alter, it may turn out that what was useful will become harmful. Therefore, the idea of relative expediency in organic nature arises. The evolutionary process is always adaptive. The polar bear can serve as an example of survival by natural selection. Under the conditions of low temperatures in the Far North, he has formed a special wool with hollow hairs that have high thermal insulation properties. The soles of their feet are lined with wool to prevent slipping on the ice and freezing. There is a swimming membrane between the toes, and the front of the paws is trimmed with stiff bristles. Big claws can hold even really strong prey. This is a necessary and sufficient condition for survival. Individuals with other mutations simply did not survive. They could not stand the harsh climatic conditions, competition with each other, and competition with other species.
However, in conditions of continuously fluctuating environmental parameters, adaptive mechanisms are not enough. Nature has provided for another way to adapt to external conditions, namely, generational change. This mechanism guarantees the survival of the species. An organism must be born, master the living space, bring in something new that is conducive to survival in the development of the living space, reproduce offspring, transfer this new acquisition to them, help them get used to the living space and die itself. Thus, the key idea of survival is that organisms must be in a state of constant adaptation to the pulsating external conditions, acquiring some mutations and passing them on by inheritance, thereby fixing effective survival mechanisms in the genetic code.
In the process of research, Ch. Darwin noticed and made another interesting practical conclusion, which is also applicable for the world of business: natural selection can be supplemented by artificial selection. In his work, he described the process of creating new breeds and varieties of cultivated plants with properties and traits over a number of generations tha are valuable to humans. As a result, artificial selection has received an important practical application, when breeders set a task and carry out selection according to several criteria, breeding plant cultures and animal breeds with given properties and characteristics. As one of the examples, Ch. Darwin speaks of farmers in Virginia whose pigs were all black. When asked why, they informed him that the pigs were eating dye roots (Lachnanthes), which caused their bones to turn pink and all but the black varieties lost their hooves. And one of the farmers added: “In each litter we select black piglets for raising, as only they have the undoubted opportunity to survive.” Gradually, this theory developed to stimulate further dynamic development of the science of selection, the theory of mutations, the theory of gene structure, and the molecular basis of heredity. Breeders obtain the necessary properties through targeted selection of starting material for breeding, hybridization, mass and individual artificial selection. For instance, when breeding animals, breeds with high productivity, vitality, resistance to diseases, and adverse environmental conditions are developed.
An amazing and expensive cat breed called the Savannah can be mentioned as an example of targeted breeding. Being a home variant of the wild serval, the savannah was bred in the 1980s. Wild cats have always been popular with the elite, and in order to protect the true cheetahs and leopards, breeders have created an alternative. The animal looks formidable and dangerous, but in fact it is affectionate and sociable. The first savannah was introduced to the world in 1986 by the Bengal breeder J. Frank. It was the result of crossing a true serval male with a domestic Siamese cat. And in 2001, the breed was officially recognized and registered.
Darwin’s theory has been criticized many times, but the idea that life developed rather than was created in a “ready-made” form does not raise doubts among the overwhelming number of scientists. One of the practical conclusions from the theory of evolution was the emergence of genetic algorithms proposed by
J. Holland in 1975. Genetic algorithms will be very useful to us for using within the framework of the paradigm proposed by the author, and their application will be discussed below.
Genetic algorithms are adaptive search-based techniques that are used to solve optimization problems. They make use of both an analogue of genetic inheritance mechanism and an analogue of natural selection, at the same time preserving biological terminology in a simplified form and basic concepts of linear algebra. The first genetic algorithm scheme was proposed by J. Holland at the University of Michigan in 1975. And Ch. Darwin’s Theory of Evolution along with the studies of L. J. Fogel, A. J. Owens, and M. J. Walsh on the evolution of simple automata intended to predict symbols in digital sequences served as the premises thereto. The new algorithm was named Holland’s reproductive plan and was later actively used as a basic algorithm in evolutionary computations. Holland’s ideas were developed by his students K. De Jong from George Mason University in Virginia and D. Goldberg from the Illinois Genetic Algorithms Laboratory. Due to them, a classical genetic algorithm was created, all operators were described and the behavior of the test functions group was investigated. It was Goldberg’s algorithm that was called the “genetic algorithm”. To understand the essence of genetic algorithms and the opportunities of their application in business, it is worth dwelling in a little more detail on the stages of this model.
Phase 1. Creation of a new population.
In the first phase, an initial population is created. Requirements for the quality of the population according to the given parameters are not critical, since in the end the algorithm will correct this problem. The most important thing is that the population should conform to the “format” and be fit for reproduction.
Phase 2. Reproduction.
It is important that the descendant (child) should be able to inherit the parents’ traits. In this case, all members of the population reproduce, and not only the survivors. Otherwise, one alpha male will stand out, whose genes will supersede all the others, and this is fundamentally unacceptable.
Phase 3. Mutations.
Mutations are similar to reproduction. A certain number of individuals are selected out of the mutants and modified in accordance with predetermined operations.
Phase 4. Selection.
At this stage, the most important process starts. The experimenter selects from the population the proportion of those who will “go further.” The proportion of those who “survived” the selection is determined in advance as a preset parameter. Then the cycle is repeated from the beginning. If the result is not satisfactory, these steps are repeated until the result becomes satisfying or until one of the following conditions is fulfilled: either the number of generations (cycles) reaches a pre-selected maximum, or the time allotted for mutation gets exhausted.
Genetic algorithms are referred to as soft computing. The term “soft computing” was introduced by L. Zadeh in 1994. This concept brings together such areas as fuzzy logic, neural networks, probabilistic reasoning, trust networks, and evolutionary algorithms, which complement each other and are used in various combinations or independently to create hybrid intelligent systems.
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