The existence of different solution approaches that generate approximations to the optimal Pareto frontiers of a multi-objective optimization problem lead to different sets of non-dominated solutions. To evaluate the quality of these solution sets, one requires a comprehensive evaluation measure to consider the features of the solutions. Despite various  valuation measures, the deficiency caused by the lack of such a comprehensive measure is  visible. For this reason, in this paper, by considering some evaluation measures, first we evaluate the quality of the approximations to the optimal Pareto front resulting from the decomposition-based multi-objective evolutionary algorithm equipped with four decomposition approaches and investigate the related drawbacks. In the second step, we use the concept of Gaussian degree of closeness to combine the evaluation measures, and hence, we propose a new evaluation measure called the quasi-Gaussian integration measure. The numerical results obtained from applying the proposed measure to the standard test functions confirm the effectiveness of this measure in examining the quality of the non-dominated solution set in a more accurate manner.

In this paper, we introduce a new continuous quantum evolutionary optimization algorithm designed for optimizing nonlinear convex functions, non-convex functions, and efficiency evaluation problems using quantum computing principles. ‎ Traditional quantum evolutionary algorithms have primarily been implemented for discrete and binary decision variables‎. ‎The proposed method has been designed as a novel continuous quantum evolutionary optimization algorithm tailored to problems with continuous decision variables‎. ‎ To assess the algorithm’s performance, several numerical experiments are conducted‎, ‎and the simulated results are compared with the Grey Wolf Optimizer and Magnet Fish Optimization search algorithm‎. ‎The simulation results indicate that the proposed algorithm can approximate the optimal solution more accurately than the two compared algorithms.

Evolutionary altruism has been a serious problem for evolutionary theory from its earliest times at mid-nineteen century. Group selection theory was its solution until 1960s.But at that time it was criticized so that it was no longer the solution to that problem. Since then theories put forward to solve the problem by individual and/or gene selection theories. Kin selection, reciprocal altruism, evolutionary game theory, the selfish gene, and indirect reciprocity are the main theories of this sort. Altruism according to these theories is apparent not real. Multi-level selection theory is introduced since 1970s that defends "new" group selection theory. In addition cultural group selection theory has come into the field. Altruism according to these theories is real. I agree with the second type of theories that real altruism has been evolved. But perhaps more in agreement with the first type I see it in the "shadow" of evolutionary egoism.

Innovations, commercialized by new or old established firms, located at the core of industrial renewal process. The innovation concept has suffered transformations, along with the evolution of the models that try to explain and understand the innovation process. The innovative process corresponds to all activities that generate technological changes and the dynamic interaction between them, not necessarily being novelties. Linier model, Chain-Linked Model and National Innovation Systems (NIS) Approach, are three models that have developed for innovation process. Innovation process can be viewed as evolutionary process. One can recognize some mechanism for innovation evolution. These are grouped into two classes; those that increase configurations variation and those that decrease it. Emergence of knowledge, knowledge flow and recombination are the mechanism to increase variation of configuration. Internal and external selections are the mechanism to selecting. Innovation operators are evolutionary operators that create new combinations of configuration and increase variation. This paper develops an evolutionary cycle in innovation process and extends evolutionary mechanisms of innovation.

Example World that is mentioned in religious texts that limbo stage, the existence of world that, on the one hand because of the (low, quality, status, etc.) is a natural physical world and on the other hand because of diversion of matter, like the universe is a single mere. This world because of rank higher than the material world is the world of the aristocracy and causality, and this is where truths and creatures of example world, infinitely are higher and stronger than creatures of the material world, and this relationship continued between creatures of wisdom world and creatures in example world. the most serious problems after the deaths is the self-enrichment of after the demise and interruption of normal body element, and foundation of this discussion is existed of time and inexistence of it (time) in Purgatory. the evolution of purgatorial from the direction of traditional and logic of revelation is undisputed and obvious. In Islamic texts related to higher worlds hereafter, that humans backs before their, full of hints and tips at the evolution of the soul is in that world.

Most differences in socio-economic opinions stem from misunderstanding the concept of “rationality. In this paper we present two different interpretation of rationality. We explain that “Evolutionary Rationality” is more conductive to development while “constructivist Rationality” divert attention from a free spontaneous market to socio economic engineering, thereby diverting development from its evolutionary path