Rapid developments in evolutionary computation, robotics, 3D-printing, and material science are enabling advanced systems of robots that can autonomously reproduce and evolve. The emerging technology of robot evolution challenges existing AI ethics because the inherent adaptivity, stochasticity, and complexity of evolutionary systems severely weaken human control and induce new types of hazards. In this paper we address the question how robot evolution can be responsibly controlled to avoid safety risks. We discuss risks related to robot multiplication, maladaptation, and domination and suggest solutions for meaningful human control. Such concerns may seem far-fetched now, however, we posit that awareness must be created before the technology becomes mature.

One of the greatest symbols of the birth of evolutionary biology is Darwin's first sketch of an evolutionary tree, above which he wrote: "I think." Not only are evolutionary trees central to how scientists conceptualize evolutionary processes, Darwin's words also capture a key aspect of evolutionary science: It is difficult to observe, forcing researchers to rely heavily on inference. In recent decades, studies of fast-growing microorganisms have allowed hypotheses about evolutionary processes to be tested experimentally (1). On page 1147 of this issue, Baym et al. (2) report a device for visualizing evolutionary branching as bacteria grow across a meter-scale agar slab. The results offer important insights into evolutionary dynamics in spatially extended systems.

Chapter 7 investigates how learning interest or relevance to them. Chapter 3 describes several techniques and evolution can interact to produce There appears to be no need to read for evolving autonomous robots adaptive individuals.

Evolutionary algorithms have demonstrated excellent results for many engineering optimization problems. In other way, recently, the chaos theory concepts and chaotic times series have gained much attention during this decade for the design of stochastic search algorithms. Differential evolution is a new evolutionary algorithm mainly having three advantages: finds the global minimum regardless of the initial parameter values, fast convergence and uses few control parameters. In this work, a new hybrid approach of Differential Evolution combined with Chaos (DEC) is presented for the optimization for path planning of mobile robots. The new chaotic operators are based on logistic map with exponential and cosinoidal decreasing.

From the bottom of the oceans to the skies above us, natural evolution has filled our planet with a vast and diverse array of lifeforms, with approximately 8 million species adapted to their surroundings in a myriad of ways. Yet 100 years after Karel Čapek coined the term robot, the functional abilities of many species still surpass the capabilities of current human engineering, which has yet to convincingly develop methods of producing robots that demonstrate human-level intelligence, move and operate seamlessly in challenging environments, and are capable of robust self-reproduction. But could robots ever reproduce? This, undoubtedly, forms a pillar of "life" as shared by all natural organisms. A team of researchers from the UK and the Netherlands have recently demonstrated a fully automated technology to allow physical robots to repeatedly breed, evolving their artificial genetic code over time to better adapt to their environment.