Evolutionary algorithmIn computational intelligence (CI), an evolutionary algorithm (EA) is a subset of evolutionary computation, a generic population-based metaheuristic optimization algorithm. An EA uses mechanisms inspired by biological evolution, such as reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the fitness function determines the quality of the solutions (see also loss function).
Tumor microenvironmentThe tumor microenvironment (TME) is the environment around a tumor, including the surrounding blood vessels, immune cells, fibroblasts, signaling molecules and the extracellular matrix (ECM). The tumor and the surrounding microenvironment are closely related and interact constantly. Tumors can influence the microenvironment by releasing extracellular signals, promoting tumor angiogenesis and inducing peripheral immune tolerance, while the immune cells in the microenvironment can affect the growth and evolution of cancerous cells.
Living systemsLiving systems are open self-organizing life forms that interact with their environment. These systems are maintained by flows of information, energy and matter. In the last few decades, some scientists have proposed that a general living systems theory is required to explain the nature of life. Such a general theory, arising out of the ecological and biological sciences, attempts to map general principles for how all living systems work.
