In this blog post, we compare genetic algorithms with natural evolution and explore the question of whether evolution represents inevitable progress or mere adaptation.
Is evolution synonymous with progress? This is a topic that has long been debated among numerous evolutionary theorists. Evolution refers to the process by which organisms alter their biological and genetic characteristics in response to environmental changes. In early evolutionary theory, the dominant view was that all life forms gradually develop from simple forms into more complex and sophisticated ones. There is also a perspective that argues evolution, which began with simple self-replicators, ultimately converges on a specific final form of life. On the other hand, there is the view that evolution is merely an adaptation to the environment, and its results are unrelated to the concepts of “progress” or “development.” For example, the evolutionary process of living organisms is argued to be nothing more than a process of optimizing opportunities for survival and reproduction, rather than having a directionality in and of itself. As such, there are diverse opinions and perspectives regarding the progressive nature of evolution, and this remains a topic of active debate in the fields of biology and philosophy.
Whether these discussions have been resolved or not, methods that actively utilize the principles of evolution to achieve optimal results are gradually being adopted across various academic disciplines and are actually yielding positive outcomes. One such method is the “genetic algorithm.” Genetic algorithms are a mathematical optimization methodology developed by borrowing the concept of evolution from biology. They apply the process of natural selection—where genetic traits of organisms change and are selected in a direction suited to the environment—to a computer model to find the optimal solution to a specific problem.
First, let’s examine what a genetic algorithm is. A genetic algorithm is an algorithm that represents possible solutions to a specific problem using a specific data structure, then gradually modifies them to approach the optimal solution. This gradual modification is implemented by allowing various solutions to solve the problem, continuously iterating through modifications and tests centered around the solution closest to the optimal one based on numerical data. Here, the data structure of the solutions can be likened to genes, and the testing of the problem and the modification of solutions based on those tests can be likened to natural selection. In this way, genetic algorithms operate based on the process of evolutionary selection, converging toward a solution close to the optimum over multiple generations.
A notable example of this model in practice is a recent research paper written by a high school student titled “A Study on the Potential for Improving Artificial Intelligence in Strategic Card Games Using the Principle of Natural Selection.” This study used the computer card game “Hearthstone” to continuously evolve a limited card deck using a genetic algorithm, while recording changes in the win rate over time. As the number of generations of the card deck increases, the deck is composed of cards that perform better in tests, and it can be observed that the average win rate gradually rises. Beyond such games, this method can be applied in any field where results can be quantified, such as using genetic algorithms to find the optimal antenna shape for high signal reception. In this way, genetic algorithms are establishing themselves as useful tools for solving complex problems across various fields.
In summary, a genetic algorithm is a problem-solving method that mimics heredity to produce an optimized model for a specific environment. This process can be explained by a similar phenomenon in evolutionary theory: “natural selection.” Natural selection is the theory that, within a specific environment, species possessing traits best suited to that environment survive longer and leave behind more offspring (i.e., genes) than those that do not. Genetic algorithms operate similarly. Models with traits best suited to the problem achieve higher performance in tests, leading to a greater number of models possessing that solution in the next generation. This is why this methodology is called a “genetic” algorithm. This is because it mimics nature almost perfectly.
However, there are significant differences between the two. The most notable difference lies in the variability of the environment. Genetic algorithms are designed to converge to a single optimal solution under a fixed problem—or environment. This prevents situations where noise can occur in the model and helps engineers derive a single conclusion tailored to a specific problem scenario. In contrast, the natural environment is complex and changes unpredictably. Even if an organism has achieved optimization in a specific environment, it may no longer be suitable as the environment changes. For example, even if an organism has adapted to a certain climate, sudden climate change could cause those characteristics to work against its survival.
Considering the claim that evolution is a progressive phenomenon alongside the core principle of natural selection—“survival of the fittest”—evolution can be described as a process of changing to become better suited to nature. In genetic algorithms, the model ultimately selected—that is, the optimal model—delivers the best results for a single test across successive generations. The model that provides the shortest and most accurate solution while solving a problem is deemed “optimal.” However, nature is in a state of constant flux. So, how should we define “optimal” in nature? Nature does not consist of single-problem scenarios. In a situation where countless problems are randomly imposed on individuals, it is very difficult to determine which form is optimal. An individual that was the optimal solution at a given moment may see its fitness plummet rapidly as the environment changes, and an individual that performs averagely across various problem factors may be outperformed by a specialized individual in an extreme situation where a single factor plays a major role.
In such an environment, evolution actually acts to increase complexity and diversity. Although humans currently occupy a significant portion of the Earth, no one can easily claim that humans are the “optimal solution” derived from hundreds of millions of years of experimentation in the laboratory that is Earth. Unlike nature, a laboratory environment is set up under controlled conditions so that only specific variables change. In such an environment, researchers can focus intensively on the specific elements they wish to explore, which is advantageous for understanding specific mechanisms of evolution. However, the natural environment is far more complex and filled with unpredictable variables; under these conditions, living organisms adapt to a constantly changing environment and survive through complex interactions. This is a fundamental difference from the laboratory-based optimization of genetic algorithms.
When comparing natural evolution to genetic algorithms, we need to reconsider the concept of “progress.” Genetic algorithms are clearly algorithms that produce models developed through evolution. In this sense, one could argue that evolution possesses a directionality of progress. In fact, over time, nature has produced life forms capable of surviving in complex and diverse environments. However, nature is not a selector that recognizes only individuals as optimal solutions; it also boasts a staggering number of individuals that are not “optimal,” yet they coexist. For example, there are insects that are frequently preyed upon by top predators but can be found almost everywhere. From this perspective, it is difficult to view evolution as having a directionality.
The debate over the directionality of evolution is a major scientific issue that cannot be resolved by any single individual. However, it can provide ideas and food for thought. “Genetic algorithms,” created using genetic phenomena, clearly provide “advanced” solutions. Whether this advancement is the same kind of advancement found in nature is another matter of debate.
