In this lesson, we will study an Introduction to Evolutionary Algorithms. Evolutionary Algorithms are fast, easy-to-implement, and robust search techniques. These algorithms are highly flexible and do not assume any specific information about the objective function. These algorithms are inspired by evolutionary biology or bionics in short and majorly follow Darwin’s principle of survival of the fittest.
The world of bionics has brought us so many amazing inventions! It’s all thanks to the application of biological or natural principles to the study and design of human systems. We’ve even been able to mimic bats and fish to create incredible inventions like radar and submarines. How cool is that?! And get this, the natural evolution of species has taught us so much about how to adapt to our environment and optimize our fitness. It’s like we can take a page from modern genetics and apply it to craft evolutionary algorithms. So, let’s just brainstorm the concept behind the family of evolutionary algorithms. Evolutionary algorithms derive their inspiration from the Darwinian Principle of the “Survival of the fittest”. According to Darwinian theory, only the fittest will survive in a given environment. And, species with competing interests will evolve over the course of time. So following the same principle, evolutionary algorithms take their inspiration from evolutionary genetics.
In the above image, don’t confuse yourself with the thought that the leopard got its meal today, but think of it as an evolution in the family of cats, where some cats have evolved dramatically bigger than my own pet cat. We will not be discussing my cat here. But, we are interested in knowing the genetic principles like mutation, crossover, and selection. Let’s dive a little deeper to understand the design methodology of evolutionary algorithms. There are three major characteristics that help evolutionary algorithms to perform complex optimization or learning tasks with the ability to evolve. We will be discussing each one of these in detail below.
Major features of Evolutionary Algorithms:
The first characteristic of these evolutionary algorithms is a population-based approach. Evolutionary Algorithms or EAs maintain a set of randomized solutions in the search space. These randomized solutions are also referred to as search agents, particles, or individuals. And this set of randomized solutions is called ‘Population‘. This population-based approach helps EAs in achieving parallel learning paradigms to find optimal solutions in the search space.
The second characteristic of EAs is to follow a fitness-oriented approach. Solutions or individuals in the population have their own genetic representation, called its code, and performance evaluation is called its fitness value. This performance evaluation depends on your objective function and the type of your optimization problem. That is, whether you want to minimize or maximize the objective function. If you want to maximize the objective function then the solution with the highest objective value is considered as the fittest. So, the core idea behind EAs is to always prefer fitter individuals. This characteristic of EAs helps in approximating the optimal solution and achieving convergence.
And the third major characteristic which provides Evolutionary Algorithms with a cutting edge is their ability to adapt to the environment by using variations in the population. These variation operators mimic the genetic gene changes and efficiently search the search space. These variations operators are mutation, crossover, and selection operators. These operators help individuals to change their position in the search space. As now you’re ready to understand a major class of these intelligent algorithms, let’s have a major timeline of the development.
Applications of Evolutionary Algorithms
The applications of Evolutionary Algorithms can be broadly studied under two major categories: Optimization Problems and Learning Problems. Optimization problems arise in almost every aspect of science, decision science, and management of resources while learning problems concern is to provide machines an “intelligence” similar to humans or about some minor percentage of what the human mind can learn. These learning problems are basically “Artificial Intelligence (AI)” problems. Different kinds of learning methods are used, some of which you may have already heard like supervised learning, unsupervised learning, and reinforcement learning. We will later discuss these problems in detail. If you’re interested in understanding these learning methods see here. (link to blog Introduction to learning methods.) Anyway, these both optimization problems and learning problems can be tackled using evolutionary algorithms. Even most of the challenging real-world optimization problems can be solved using evolutionary algorithms which was not possible earlier using traditional optimization techniques. Here below we list some of the applications from various academic disciplines where these intelligent algorithms can be used.
- Evolutionary Algorithms in Machine Learning: Evolutionary algorithms are powerful tools that are widely used in machine learning to optimize the performance of complex models, such as neural networks. These algorithms work by iteratively evaluating different combinations of hyperparameters and architecture, selecting the best ones, and then recombining and mutating them to generate new solutions. By doing this over many generations, the algorithm can converge on an optimal solution that maximizes performance on a given task. The search space for optimal hyperparameters and architecture can be extremely large and complex, making manual optimization impractical. Evolutionary algorithms offer a highly efficient and effective solution to this problem. These algorithms can explore a large and diverse range of possible solutions, identifying those that show the most promise and combining them to create even better solutions. Evolutionary algorithms have become a standard technique in the field of machine learning, by allowing researchers to explore a much larger and more diverse space of possible solutions, these algorithms are helping to push the boundaries of what is possible in machine learning and artificial intelligence.
- Evolutionary Algorithms in Robotics: Robotics is a rapidly developing field that requires a lot of careful planning and precision to create robots that are effective and efficient. This is where evolutionary algorithms come in. These algorithms can be used to design robots with optimal physical characteristics and behaviors for specific tasks. For example, researchers may use evolutionary algorithms to find the best design for a robot that needs to navigate through tricky terrain. By testing out a variety of different leg or wheel configurations, the algorithm can identify the design that best suits the task at hand. Similarly, evolutionary algorithms can be used to design robots that are better suited for specific environments, such as deep-sea exploration or space exploration. One of the key advantages of using evolutionary algorithms in robotics is that they can explore a very large space of possible solutions, far beyond what humans could feasibly consider. This allows for the development of robots that are highly optimized and tailored to specific tasks, which can ultimately improve their performance and efficiency. So, while we may not be facing a robot apocalypse just yet, the use of evolutionary algorithms is certainly helping to push the boundaries of what robots can do.
- Evolutionary Algorithms in Financial Forecasting: Financial forecasting is an important application area for evolutionary algorithms. These algorithms can be used to analyze large datasets of financial information and identify patterns and trends that can inform investment decisions and improve financial performance. These algorithms handle complex and nonlinear relationships between different financial variables. This makes them well-suited for tasks such as stock price prediction, where the relationship between different market indicators can be highly non-intuitive. Evolutionary algorithms can also be used to optimize investment strategies, by selecting a portfolio of assets that maximizes returns while minimizing risk. By using historical data to simulate different investment strategies and evaluating their performance using evolutionary algorithms, investors can identify strategies that are likely to perform well in the future. The use of evolutionary algorithms in financial forecasting is helping to improve investment decision-making and optimize financial performance. As financial data becomes increasingly complex and the need for accurate forecasting grows, the use of these algorithms is likely to become even more widespread in the financial industry.
- Evolutionary Algorithms in Bio-informatics: Evolutionary algorithms are increasingly being used in bioinformatics to analyze biological data and gain insights into complex biological systems. These algorithms can help identify important DNA and protein sequences, predict protein structures, and optimize drug discovery efforts. Additionally, evolutionary algorithms can aid in the alignment and comparison of genetic sequences, phylogenetic analysis, prediction of gene function, and identification of genetic markers for diseases. The use of these algorithms can also facilitate the design of optimized DNA sequences for synthetic biology and the prediction of protein-protein interactions. With the ability to handle large datasets and complex relationships between different variables, evolutionary algorithms have the potential to revolutionize our understanding of biological systems and contribute to new breakthroughs in medicine and biotechnology.
Apart from the above-mentioned applications the use of evolutionary algorithms is widespread in control theory, game theory, image processing, signal processing, planning and scheduling, multi-objective optimization, and many more. In short, if you’re having a convex problem which is involving various model parameters, evolutionary algorithms can become a handy choice for you. We will be discussing some popular evolutionary algorithms in detail and try to implement these algorithms in some computing environments like Python or MATLAB.
I hope you enjoy reading the introduction to evolutionary algorithms. Leave your comments and insights in the comment section below. Happy Learning!