Neuroevolution is a subfield of artificial intelligence and machine learning that involves the use of evolutionary algorithms to evolve neural networks for various tasks. The field has a rich history spanning several decades, and has been influenced by a number of key researchers and developments. Here is a brief history of neuroevolution: 1987: John Holland introduced the concept of genetic algorithms, which can be used to optimize complex problems by applying the principles of natural selection to a population of candidate solutions. 1989: Kenneth De Jong and others proposed the use of genetic algorithms to evolve neural networks, in a process known as neuroevolution. 1993: Faustino Gomez and Risto Miikkulainen introduced a method called "neuroevolution of augmenting topologies" (NEAT), which evolves both the structure and weights of neural networks. 1997: The first international conference on artificial neural networks and genetic algorithms (ICANNGA) was held, bringing together researchers working in the field of neuroevolution. 2002: A team at the University of Texas, Austin used NEAT to evolve neural networks for playing Atari games, demonstrating the potential of neuroevolution for challenging problems in reinforcement learning. 2007: A team at Cornell University used a variant of NEAT called HyperNEAT to evolve neural networks for controlling robots, demonstrating the potential of neuroevolution for real-world applications. 2017: Uber AI Labs used a variant of neuroevolution called "deep neuroevolution" to evolve neural networks for playing video games, achieving state-of-the-art results on a number of benchmark tasks. Today, neuroevolution continues to be an active area of research, with applications in fields ranging from robotics and control to natural language processing and computer vision. Advances in computational power and algorithmic techniques are likely to further accelerate the development of this field in the coming years.