Discusses whether evolution means development or just adaptation to the environment, and compares how genetic algorithms mimic the principles of evolution to achieve optimization. The problem of defining the direction of evolution in nature’s changing environment is complex, but genetic algorithms have been successfully applied to produce optimal solutions under fixed conditions.
Is evolution progress? This is a topic that has been debated for a long time, with many evolutionists sharing their opinions. Evolution is the process by which organisms change their biological and genetic characteristics in response to changes in their environment. In early evolutionary theory, the idea was that all living things develop from simple forms to more complex and advanced ones. One school of thought holds that evolution, starting from simple self-replicating organisms, ultimately converges on a final form of life. On the other hand, there is also the view that evolution is simply adaptation to the environment, and that the outcome has nothing to do with the concept of “progress” or “development. For example, the evolutionary process of life does not have a direction in itself, but simply optimizes the chances of survival and reproduction. As you can see, there are many different opinions and perspectives on evolutionary development, and it’s still a hotly debated topic in biology and philosophy.
Whether or not these debates have been settled, methods that actively utilize evolutionary principles to achieve optimal results are increasingly being used in a variety of disciplines, and they’re working well. One such method is genetic algorithms. Genetic algorithms are a mathematical optimization methodology that borrows from the concept of evolution in biology. It applies the process of natural selection, in which an organism’s genetic traits change to adapt to its environment, to a computer model to find the optimal solution to a specific problem.
First, let’s understand what a genetic algorithm is. A genetic algorithm is an algorithm that represents the possible solutions to a given problem in some form of data structure, and then uses incremental variations to get closer to the optimal solution. This is accomplished by allowing different solutions to solve the problem, and then iterating and testing them until a solution that is close to optimal on the quantified data is found. The data structure of the solutions can be analogized to genes, and the testing of the problem and the evolution of solutions based on the testing can be analogized to natural selection. In this way, genetic algorithms work in a way that is inspired by the evolutionary selection process, converging to a solution that is close to optimal over many generations.
An example of this model in action is a recent paper by a high school student titled “A Study on the Possibility of Improving AI for Strategy Card Games Using the Principle of Natural Selection”. This study used the computer card game ‘Hearthstone’ to continuously evolve a limited deck of cards using a genetic algorithm and recorded the changes in the winning rate. As the deck’s generation increases, the deck is increasingly composed of cards that perform well in testing, and the average win rate gradually increases. In addition to games like this, genetic algorithms can be used to find the optimal antenna shape for high radio reception, for example, in any field where the results can be quantified. As you can see, genetic algorithms are becoming a useful tool for solving complex problems in a variety of fields.
In summary, a genetic algorithm is a problem-solving method that mimics genetics to produce an optimized model for a given environment. This process can be explained by a similar phenomenon in evolutionary theory. It’s called “natural selection. Natural selection is the theory that within a given environment, species with traits best suited to that environment will survive longer and leave more offspring (i.e. genes) than those without. Genetic algorithms are similar to this. The model with the best properties for the problem performs well in testing, and more models with that solution exist in the next generation. This is why the methodology is called a “genetic” algorithm. It mimics genetics almost perfectly.
However, there are important differences between the two. The main one is the variability of the environment. In the case of genetic algorithms, they are designed to converge to a single optimal solution while the problem, or environment, is fixed. This prevents the model from introducing noise and allows engineers to draw a single conclusion for a particular problem situation. Natural environments, on the other hand, are complex and change unpredictably. Even if an entity is optimized for a particular environment, it may no longer be suitable as the environment changes. For example, an organism may have adapted to a climate to a certain extent, but a sudden change in climate could turn that trait against its survival.
Combining the idea that evolution is a progressive phenomenon with the idea of survival of the fittest, which is the basic goal of natural selection, we can say that evolution is simply a process of adaptation. The model that is finally adopted by the genetic algorithm, the optimal model, is the one that produces the best results for a single test over generations. The model that solves the problem and gives the shortest and most accurate answer is the “optimal” one. But nature is constantly changing. How do we define “optimal” in nature? Nature does not consist of a single problem situation. Many problems are randomly imposed on individuals, and it’s very difficult to determine what form is optimal. An individual that is optimal in the moment may rapidly lose fitness as the environment changes, or an individual that performs well on average across a range of problem factors may lose out to a specialized individual in extreme situations where a single factor plays a large role.
In such environments, evolution works to increase complexity and diversity. While humans now occupy a significant portion of the planet, no one can easily say that we are the “optimal solution” to hundreds of millions of years of experimentation in a laboratory. Unlike nature, laboratory environments are set up to vary only certain variables under controlled conditions. This is advantageous for understanding specific mechanisms of evolution because it allows us to focus on the factors we want to explore. However, natural environments are more complex and full of unpredictable variables, and under these conditions, organisms adapt to their ever-changing environment and survive through complex interactions. This is fundamentally different from the experimental optimization of genetic algorithms.
When comparing natural evolution to genetic algorithms, we need to rethink the concept of evolution. A genetic algorithm is clearly an algorithm that produces a model that has evolved through evolution. In this sense, we could even say that evolution has a progressive direction. However, nature is not a selector that only recognizes the optimal individuals, and even those that are not “optimal” live alongside us in manageable numbers. For example, there are insects that are frequently eaten by top predators, but can be found almost everywhere. From this perspective, it’s hard to see evolution as directional.
The debate about the direction of evolution is a huge scientific debate that cannot be concluded by one person, but it can provide ideas and food for thought. Genetic algorithms created using hereditary phenomena certainly provide “advanced” answers. Whether this progress is the same progress in nature is another debate.
