In this blog post, we compare genetic algorithms with natural evolution and explore the question of whether evolution is inevitable progress or just simple adaptation.
Is evolution progress? This is a topic that many evolutionists have debated for a long time. Evolution refers to the process by which living organisms change their biological and genetic characteristics in response to environmental changes. In the early days of evolutionary theory, the prevailing idea was that all living things evolve from simple forms to increasingly complex and advanced forms. There is also a view that argues that evolution, which began with simple self-replicators, ultimately converges on a final form of life. On the other hand, there is also the view that evolution is simply an adaptation to the environment, and that the result is irrelevant to the concept of “progress” or “development.” For example, the evolutionary process of living organisms is simply a process of optimizing the chances of survival and reproduction, rather than having a direction in itself. As such, there are various opinions and perspectives on the progressiveness of evolution, which are still actively discussed in the fields of biology and philosophy.
Whether or not these discussions have been sorted out, methods that actively utilize the principles of evolution to achieve optimal results are increasingly being used in various academic fields and are actually producing good results. One of these is the “genetic algorithm.” The genetic algorithm is a mathematical optimization methodology that was created by borrowing the concept of evolution from biology. This is a method of finding the optimal solution to a specific problem by applying the process of natural selection, in which the genetic characteristics of living organisms change and are selected in a direction suitable for the environment, to a computer model.
First, let’s take a look at what genetic algorithms are. Genetic algorithms are algorithms that express possible solutions to a specific problem as a certain type of data structure and then gradually modify it to get closer to the optimal solution to the problem. Gradual transformation is implemented by allowing various solutions to solve the problem and continuing to transform and test solutions based on the optimal solution on the numerical data. Here, the data structure of the solutions can be likened to a gene, and the testing of the problem and the transformation of the solution based on the testing can be likened to natural selection. As such, genetic algorithms operate in a way inspired by the process of evolutionary selection, converging on an optimal solution over several generations.
A study in which this model was actually applied is the “Study on the Possibility of Improving AI in Strategy Card Games Using the Principle of Natural Selection,” which is famous for being a short essay written by a high school student. This is a study that records the changes in the winning rate while continuously developing a limited card deck using the computer card game “Hearthstone” according to a genetic algorithm. As the number of card decks increases, the deck is composed of cards that perform well in tests, and the average winning rate gradually increases. In addition to games like these, there are many other areas where this method can be used, such as finding the optimal antenna shape for high signal reception using genetic algorithms. Genetic algorithms are becoming a useful tool for solving complex problems in various fields.
In short, genetic algorithms are a problem-solving method that produces an optimized model by mimicking heredity for a specific environment. This process can also be explained by a similar phenomenon in evolutionary theory: natural selection. Natural selection is the theory that species with the most suitable traits for a specific environment survive longer than species without such traits and leave more offspring (i.e., genes). The genetic algorithm is similar to this. Models with the most suitable properties for the problem perform well in the test, and the next generation will have more models with those solutions. This is why this methodology is called the “genetic” algorithm. It almost perfectly mimics heredity.
However, there are important differences between the two. The most obvious difference is in the variability of the environment. Genetic algorithms are designed to converge to a single optimal solution while the problem, or environment, is fixed. This prevents situations where noise can occur in the model and helps engineers to draw a single conclusion that is specific to the problem situation. In contrast, the natural environment changes in complex and unpredictable ways. Even if an organism has achieved optimization in a certain environment, it may no longer be suitable as the environment changes. For example, even if a creature has adapted to a certain climate, sudden climate change may make its characteristics disadvantageous for survival.
If we consider the claim that evolution is a progressive phenomenon and the basic principle of natural selection, “survival of the fittest,” together, we can say that evolution is simply a phenomenon that changes to suit nature. The final model adopted in the genetic algorithm, that is, the optimal model, produces the best results for a single test generation after generation. The model that produces the shortest and most accurate answer while solving the problem is the “optimal” one. However, nature is constantly changing. So how should “optimal” be defined in nature? Nature is not made up of a single problem situation. It is very difficult to determine which form is optimal in a situation where numerous problems are randomly imposed on an individual. An individual that was optimal at a moment in time may rapidly become less suitable as the environment changes, or an individual that performs averagely across a variety of problem factors may be outclassed by a specialized individual in an extreme situation where a single factor plays a major role.
In such an environment, evolution works to increase complexity and diversity. Although humans currently occupy a significant portion of the Earth, no one can easily say that humans are the “optimal solution” derived from hundreds of millions of years of experimentation in the laboratory. Unlike nature, the laboratory environment is set up to change only certain variables under controlled conditions. In such an environment, it is possible to focus on only the elements that are to be explored, which is advantageous for understanding the specific mechanisms of evolution. However, the natural environment is more complex and full of unpredictable variables, and in such conditions, organisms adapt to the ever-changing environment and survive through complex interactions. This is fundamentally different from the experimental optimization of genetic algorithms.
When comparing the evolution of nature and genetic algorithms, we need to think about the concept of progress once again. Genetic algorithms are algorithms that produce models that have been developed through evolution. In this respect, we can say that evolution has a progressive direction. In fact, nature has created living organisms that can survive in complex and diverse environments over time. However, nature is not a selector that only recognizes individuals as optimal, and individuals that are not “optimal” also live together with a number of individuals that are worth processing. For example, there are insect species that are often eaten by higher predators but can be found almost anywhere. From this perspective, it is difficult to see that evolution has a direction.
The debate on the direction of evolution is a major scientific issue that cannot be concluded by one person alone. However, we can provide ideas and food for thought. The “genetic algorithm,” which is created using genetic phenomena, clearly provides an “advanced” answer. Whether this advancement is the same in nature will be another topic of debate.
