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Learn how evolutionary algorithms work and how they are used in machine learning to optimize models, solve complex problems, and drive advancements in AI.
Life on Earth has evolved over millions of years, and it’s through this process that living things have adapted to survive and thrive in their environments. Take giraffes, for example. They developed long necks over many generations to reach leaves high up in trees, where other animals can’t. This is driven by natural selection, where helpful traits become more common as those with them are more likely to survive.
Simply put, organisms that are better suited to their surroundings have a better chance of surviving and passing on their useful traits to their offspring. Over time, these small changes add up, helping species handle even the most challenging conditions.
Evolutionary algorithms (EA) fall under the umbrella term of “Machine Learning”. Just like evolution, EA picks the best solutions from a group, makes small changes, and checks if those changes make things better. By repeating this process over many generations, it finds the best solutions to complex problems across various different fields.
Thanks to their versatility, evolutionary algorithms are an interesting area of research, with ongoing studies aimed at applying them to fields like engineering, medicine, and environmental science. In this article, we’ll take a closer look at evolutionary algorithms, how they work, and where they’re used. Let’s get started!
Evolutionary algorithms explained
Evolutionary algorithms are optimization techniques that can be used within the realm of machine learning to find the best possible solution or model for a given task. They typically start off with a group of potential solutions and work to improve them over many iterations based on how well they solve the problem.
For instance, imagine trying to design the most fuel-efficient car. The algorithm starts with a variety of car designs, tests each one to see how well it performs, and then gradually improves the designs by combining the best features and making small adjustments over many rounds until it finds the best possible design.
There are several types of evolutionary algorithms, each with its own way of representing and improving solutions, but all follow this basic approach. Here are some common types of evolutionary algorithms:
Genetic algorithms: In this type of evolutionary algorithm, solutions are represented as simple coded strings, like sequences of numbers or symbols. Improvement happens by mixing parts of two good solutions (similar to combining traits from parents) and making small random changes to create new variations.
Genetic programming: It focuses on building solutions as tree-like structures. It is especially useful when the solution requires a sequence of steps or instructions.
Evolution strategies: Here, the focus is on learning how big or small the changes should be when updating solutions. This helps the algorithm improve its results more efficiently over time.
Differential evolution: With this approach, new solutions are formed by combining parts of different existing solutions. It works particularly well for problems where the answers are numbers that can vary continuously.
Understanding how evolutionary algorithms work
Evolutionary algorithms use an iterative process to find the best solution to a problem. You can think of it like nature’s way of refining species over generations, where each cycle helps improve the population.
Though different types of evolutionary algorithms exist, they generally follow these key steps:
Initialization: The algorithm starts by generating an initial set of possible solutions. This forms the starting point for the evolutionary process
Fitness evaluation: Each solution is assessed using a fitness function that measures how well it solves the problem. Solutions that perform better receive higher scores and have a greater chance of being chosen for the next steps.
Reproduction: New solutions are created through two main methods: crossover and mutation. Crossover combines features from two-parent solutions, while mutation introduces small random changes to explore new possibilities.
Replacement: The newly created solutions replace some or all of the older solutions. This step determines which solutions survive and move on to the next generation.
Termination: The process of reproduction and replacement continues until a stopping condition is met. This could be reaching a set number of iterations, achieving a satisfactory fitness level, seeing no further improvements, or hitting computational limits.
Fig 1. A look at how evolutionary algorithms work. Image by author.
A look at evolutionary algorithm applications
Now that we have a better understanding of what evolutionary algorithms are and how they work, let’s walk through some of their real-world applications in different industries.
Bio-inspired algorithms used at windmill power plants
One of the most critical aspects of wind energy generation is the placement of wind turbines. The efficiency of a wind farm can vary significantly based on how turbines are positioned relative to each other and wind conditions. Traditional design methods often struggle to handle the many external variables involved.
Evolutionary algorithms are a great way to come up with and test lots of different wind farm layouts. They look at things like wind patterns, the types of turbines, and the available land to find the best setup. With each round of testing, the design gets better, leading to more energy, lower costs, and better use of land. After several iterations, the final design can deliver a better energy output than one made manually.
Fig 2. A windmill power plant.
Optimization techniques in vehicle design
Designing cars that are safe, fuel-efficient, and affordable is a key priority for today’s automakers. Evolutionary algorithms can help speed up this process by testing many design options and gradually improving them to find the best one. This can help manufacturers find the best balance between performance and efficiency.
An interesting example is the use of evolutionary algorithms to design the core vehicle structure, known as the Body-in-White (BIW). The main goal in this design process is to reduce the body’s weight while maintaining the same durability and strength.
Genetic algorithms can explore different structural variations with minor changes. These designs are then tested based on factors like strength, weight, durability, and regulatory standards to identify the best option.
Fig 3. The Body-in-White design structure of a vehicle (source).
Evolutionary strategies in scheduling at hospitals
Researchers are actively studying how evolutionary algorithms can help in healthcare, such as scheduling hospital staff, improving treatment plans, and discovering new medicines. These algorithms work well because they can handle large amounts of data and consider things like fatigue and stress.
For example, genetic algorithms have been tested to improve nurse scheduling in hospitals. One fascinating study used a mix of genetic algorithms and local search techniques to create schedules that reduce nurse fatigue. The results showed a 10% drop in fatigue and made the scheduling process 98% faster, cutting the time from over an hour to just a little over a minute.
Fig 4. Manual scheduling vs using a genetic algorithm (GA) (source).
The role of evolutionary techniques in computer vision
Other than machine learning applications, evolutionary algorithms can also be used in areas like computer vision - the field of AI that enables machines to interpret and analyze visual data such as images and videos. Ongoing research explores their potential in tasks like image segmentation, object detection, and feature extraction.
These algorithms work by testing many possible solutions and selecting the best ones, which makes them flexible and able to handle complex and varied visual data. For example, researchers have used evolutionary algorithms to improve AI systems that analyze medical images like chest X-rays.
These AI systems, known as convolutional neural networks (CNNs), act like expert radiologists by scanning images to spot signs of diseases such as COVID-19. The evolutionary algorithm helps by automatically designing and fine-tuning the CNN’s structure and settings, testing many versions, keeping the best performers, and gradually creating more accurate models. This process leads to AI tools that can assist doctors in diagnosing COVID-19 more quickly and accurately.
Pros and cons of evolutionary algorithms
Here are some of the key advantages that evolutionary algorithms offer:
Global search ability: Evolutionary algorithms explore many parts of the solution space simultaneously, making it less likely to get stuck in poor solutions.
Robustness: In uncertain and changing environments, the evolutionary algorithm’s randomness and diverse solutions help the system maintain consistent performance.
Flexibility: Evolutionary algorithms can be adjusted to work well on many different kinds of problems by changing how solutions are represented and how success is measured.
While these algorithms have many benefits, it’s also important to be aware of their limitations. Here are some common disadvantages of evolutionary algorithms:
Computational cost: Evaluating many possible solutions repeatedly can require powerful and expensive computing resources, making them slow or costly for some applications.
Parameter sensitivity: The success of evolutionary algorithms often depends heavily on choosing the right initial settings, like population size, mutation rate, and selection methods. Poor choices can hurt performance.
Slow convergence: They might require many iterations to improve solutions, especially for very complex problems, which can lead to longer run times compared to other methods.
Fitness function sensitivity: Poor fitness function design can cause algorithms to exploit flaws rather than solve the intended problem, misleading developers about actual performance.
Key takeaways
Evolutionary algorithms are a great option for solving tough problems that other methods may struggle to handle. Research has shown that they improve gradually through iteration and can adapt to a wide variety of tasks. As AI continues to advance and evolve, these algorithms are expected to play an even bigger role in developing smart, efficient, and self-learning technologies across many different applications.
