生存斗争：时间、记忆与代码膨胀
The Struggle for Existence: Time, Memory and Bloat
AI总结：
本文提出一种基于时空多智能体觅食生态系统的隐式内源性进化优化方法，通过有限内存与执行时间约束模拟真实生物系统，验证了该方法在发现适应性和新颖性解决方案方面的有效性，并为检验中性代码膨胀假说提供了实验途径。
摘要：
将基于时空多智能体的觅食生态系统模型与智能体行为的线性遗传编程规则相结合，可产生基于“自然选择”的隐式、内源性目标函数和选择算法。通过应用于人工觅食生态系统，检验了这种利用遗传程序隐式优化研究生物系统的方法，并与已有的生物学、生态学和随机基因扩散模型进行了比较。有限的程序内存和执行时间约束模拟了物理和生物系统的实时性与并发性特性，并对优化算法进行了压力测试。智能体程序的相对适应度以及由此产生的种群效率作为这些约束的函数，用于衡量优化的有效性和效率。新颖的解决方案证实了优化过程的创造性，并为实验检验中性代码膨胀假说提供了独特的机会。因此，这种对空间交互、遗传编程智能体进行隐式、内源性进化优化的方法被证明具有新颖性，与生物系统一致，并且在发现适应性和新颖性解决方案方面既有效又高效。
英文摘要：
Combining a spatiotemporal, multi-agent based model of a foraging ecosystem with linear, genetically programmed rules for the agents' behaviors results in implicit, endogenous, objective functions and selection algorithms based on "natural selection". Use of this implicit optimization of genetic programs for study of biological systems is tested by application to an artificial foraging ecosystem, and compared with established biological, ecological, and stochastic gene diffusion models. Limited program memory and execution time constraints emulate real-time and concurrent properties of physical and biological systems, and stress test the optimization algorithms. Relative fitness of the agents' programs and efficiency of the resultant populations as functions of these constraints gauge optimization effectiveness and efficiency. Novel solutions confirm the creativity of the optimization process and provide an unique opportunity to experimentally test the neutral code bloating hypotheses. Use of this implicit, endogenous, evolutionary optimization of spatially interacting, genetically programmed agents is thus shown to be novel, consistent with biological systems, and effective and efficient in discovering fit and novel solutions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
