人工生命(Artificial Life)于1993年秋已成为统一的研究人工系统的科学信息交流论坛,具有自然生命系统的行为特征,通过合成或模拟使用计算机(软件),机器人(硬件)和物理化学(人脑)的意义。每一期都有关于人工生命的前沿研究,这些研究将提高我们对生命系统各个方面的认识,如:人工化学和生命的起源、系统与合成生物学、感知,认知和行为、群体的集体行为、进化与生态动力学、开放性和创造性、社会组织与文化演变、对社会及科技的影响、应用于生物学、医学、商业、教育或娱乐。 官网地址:http://dblp.uni-trier.de/db/journals/alife/

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Genetic algorithms, computer programs that simulate natural evolution, are increasingly applied across many disciplines. They have been used to solve various optimisation problems from neural network architecture search to strategic games, and to model phenomena of adaptation and learning. Expertise on the qualities and drawbacks of this technique is largely scattered across the literature or former, motivating an compilation of this knowledge at the light of the most recent developments of the field. In this review, we present genetic algorithms, their qualities, limitations and challenges, as well as some future development perspectives. Genetic algorithms are capable of exploring large and complex spaces of possible solutions, to quickly locate promising elements, and provide an adequate modelling tool to describe evolutionary systems, from games to economies. They however suffer from high computation costs, difficult parameter configuration, and crucial representation of the solutions. Recent developments such as GPU, parallel and quantum computing, conception of powerful parameter control methods, and novel approaches in representation strategies, may be keys to overcome those limitations. This compiling review aims at informing practitioners and newcomers in the field alike in their genetic algorithm research, and at outlining promising avenues for future research. It highlights the potential for interdisciplinary research associating genetic algorithms to pulse original discoveries in social sciences, open ended evolution, artificial life and AI.

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Researching the conditions for the emergence of life -- not necessarily as it is, but as it could be -- is one of the main goals of Artificial Life. Artificial Chemistries are one of the most important tools in this endeavour, as they allow us to investigate the process by which metabolisms capable of self-reproduction and -- ultimately -- of evolving, might have emerged. While previous work has shown promising results in this direction, it is still unclear which are the fundamental properties of a chemical system that enable emergent structures to arise. To this end, here we present an Artificial Chemistry based on Combinatory Logic, a Turing-complete rewriting system, which relies on a minimal set of possible reactions. Our experiments show that a single run of this chemistry starting from a tabula rasa state discovers with no external intervention a wide range of emergent structures, including autopoietic structures that maintain their organisation unchanged, others that grow recursively, and most notably, patterns that reproduce themselves, duplicating their number on each cycle. All of these structures take the form of recursive algorithms that acquire basic constituents from the environment and decompose them in a process that is remarkably similar to biological metabolisms.

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Researching the conditions for the emergence of life -- not necessarily as it is, but as it could be -- is one of the main goals of Artificial Life. Artificial Chemistries are one of the most important tools in this endeavour, as they allow us to investigate the process by which metabolisms capable of self-reproduction and -- ultimately -- of evolving, might have emerged. While previous work has shown promising results in this direction, it is still unclear which are the fundamental properties of a chemical system that enable emergent structures to arise. To this end, here we present an Artificial Chemistry based on Combinatory Logic, a Turing-complete rewriting system, which relies on a minimal set of possible reactions. Our experiments show that a single run of this chemistry starting from a tabula rasa state discovers with no external intervention a wide range of emergent structures, including autopoietic structures that maintain their organisation unchanged, others that grow recursively, and most notably, patterns that reproduce themselves, duplicating their number on each cycle. All of these structures take the form of recursive algorithms that acquire basic constituents from the environment and decompose them in a process that is remarkably similar to biological metabolisms.

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