In this blog post, we’ll compare genetic algorithms with natural evolution and explore whether evolution is inevitable progress or a continuous process of adaptation to the environment.
Is evolution synonymous with progress? This question has long been debated by countless evolutionary biologists and philosophers. Evolution refers to the phenomenon in which the biological and genetic characteristics of organisms change across generations as they adapt to environmental changes. Early evolutionary theories strongly tended to view all life forms as developing from simple to increasingly complex and sophisticated forms. There was also a perspective that life, which began as simple self-replicators, ultimately converges toward a single final form. In contrast, today the view that evolution is a process of adaptation to the environment—rather than progress toward a specific goal—is widely accepted. In other words, evolution is merely a process in which traits advantageous for survival and reproduction are selected, and it is difficult to interpret its results through value judgments such as “progress” or “development.” As such, there are diverse opinions and perspectives regarding the directionality and developmental nature of evolution, and these discussions continue actively in the fields of biology and philosophy.
Regardless of these debates, methods that actively utilize the principles of evolution to achieve optimal results are widely applied across various academic and industrial fields and are, in fact, yielding outstanding results. A prime example of this is the “genetic algorithm.” Genetic algorithms are a representative optimization technique developed by drawing inspiration from the principles of evolution and natural selection found in biology. It works by applying the process—in which genetic traits of organisms change and individuals best suited to their environment are selected—to a computer model to search for optimal solutions to complex problems.
First, let’s examine what a genetic algorithm is. A genetic algorithm is an algorithm that represents possible solutions to a specific problem as a specific type of data structure and then, through iterative selection and mutation, gradually converges toward the optimal solution. In this process, various solutions are repeatedly evaluated, and new solutions are generated based on those that demonstrate high performance. This enables the continuous search for numerically superior results. Here, each solution can be likened to the genes of an organism, and the process of evaluating and selecting solutions plays a role similar to natural selection. Furthermore, new solutions are generated through operations such as crossover and mutation, processes that are also inspired by biological evolution. Thus, genetic algorithms are optimization techniques that mimic the principles of natural evolution, gradually converging toward superior solutions over multiple generations.
Genetic algorithms are actually used in a wide range of fields. In the past, they gained widespread recognition through applications such as deck building for card game AI and research on optimizing game strategies. For example, a study was published that used genetic algorithms to iteratively improve decks in the computer card game “Hearthstone” and analyze changes in win rates. The results showed that as the number of generations of the deck increased, higher-performing cards were naturally selected, leading to a gradual improvement in the average win rate. Recently, the scope of these applications has expanded further, and genetic algorithms and evolutionary computation techniques are now actively used in various fields, including antenna design, industrial process optimization, robot control, logistics route planning, hyperparameter optimization for machine learning models, and the discovery of new drug candidates. For any problem where results can be quantified and evaluated, genetic algorithms remain a highly useful optimization tool.
In summary, a genetic algorithm is a problem-solving method that applies the principles of biological evolution to find optimal solutions in specific environments. This process has a structure very similar to “natural selection” as described in evolutionary biology. Natural selection refers to the process by which individuals with traits better suited to a specific environment survive longer and produce more offspring than those without such traits, thereby passing those traits on to the next generation.
Similarly, in genetic algorithms, models with superior problem-solving capabilities receive higher evaluations and pass on more of those characteristics to the next generation. For this reason, this methodology is called a “genetic algorithm.” However, it is more accurate to understand it not as a direct replication of biological genetic processes, but as an optimization technique designed by drawing inspiration from the principles of natural evolution and natural selection.
Nevertheless, there are significant differences between genetic algorithms and natural evolution. The most notable difference lies in the variability of the environment. In genetic algorithms, the problem to be solved—that is, the environment—is largely fixed, and the algorithm is designed to find a single optimal solution. This structure helps minimize unnecessary variables, enabling the efficient identification of the solution best suited to a specific problem. In contrast, the natural environment is highly complex and constantly changing. A trait that was once considered optimal in a specific environment may no longer be advantageous once the environment changes. For example, even if an organism has adapted to a certain climate and developed traits that are advantageous for survival, those same traits may actually work against its survival if a sudden climate change occurs. Therefore, natural evolution can be viewed not as a process moving toward a fixed goal, but rather as a process of constantly adapting to a changing environment.
When considering whether evolution is a phenomenon that signifies progress or merely a process of adaptation, it is necessary to examine “survival of the fittest,” a core concept of natural selection. In genetic algorithms, the model ultimately selected is the optimal model that demonstrates the best performance for a given problem. Under identical conditions, the solution that resolves the problem most concisely and accurately is considered the optimal solution. However, nature is not an environment where only a single problem exists. In nature, countless environmental factors and unpredictable variables interact simultaneously. Therefore, it is difficult to definitively declare any single organism as absolutely optimal. An individual that demonstrated the greatest adaptability at a specific point in time may see its fitness decline significantly as the environment changes, while an individual that typically exhibits average performance may actually demonstrate greater survivability in the face of unexpected environmental shifts. Thus, fitness in nature is not determined by a single fixed standard but continuously changes in response to environmental shifts.
In such an environment, evolution often acts to expand the diversity and complexity of life rather than simply proceeding toward a single optimal solution. Although humans are currently a species with immense influence on Earth’s ecosystem, this does not mean we can conclude that humans are the final result of billions of years of evolution or the absolute optimal solution. In a laboratory, research is conducted under controlled conditions by varying only specific variables, allowing for relatively clear confirmation of desired results. In contrast, the natural environment is a complex system in which countless variables interact simultaneously. Within this environment, living organisms compete and cooperate with one another, repeatedly adapting to unpredictable changes. This is why genetic algorithms, which seek optimal solutions in controlled environments, are fundamentally different from natural evolution.
