It has been also demonstrated that emotional In its basic underpinning assumptions, cognitive load states (e.g., negative mood or anxiety) directly influence theory relies on the analogy between the information cognitive task performance and the operation of working processing aspects of evolution by natural selection and memory, while less evidence exists about the effect of the human cognition (Sweller & Sweller, 2006). It considers emotional content of the processed information (e.g., both biological evolution and human cognition as Kensinger & Corkin, 2003).

In this paper, we combine a novel Sequential Graph Coloring Heuristic Algorithm (SGCHA) with a non-systematic method based on a cultural algorithm to solve the graph coloring problem (GCP). The GCP involves finding the minimum number of colors for coloring the graph vertices such that adjacent vertices have distinct colors. In our solving approach, we first use an estimator which is implemented with SGCHA to predict the minimum colors. Then, in the non-systematic part which has been designed using cultural algorithms, we improve the prediction. Various components of the cultural algorithm have been implemented to solve the GCP with a self adaptive behavior in an efficient manner. As a result of utilizing the SGCHA and a cultural algorithm, the proposed method is capable of finding the solution in a very efficient running time. The experimental results show that the proposed algorithm has a high performance in time and quality of the solution returned for solving graph coloring instances taken from DIMACS website. The quality of the solution is measured here by comparing the returned solution with the optimal one.

This article deals with Part family formation problem which is believed to be moderately complicated to be solved in polynomial time in the vicinity of Group Technology (GT). In the past literature researchers investigated that the part family formation techniques are principally based on production flow analysis (PFA) which usually considers operational requirements, sequences and time. Part Coding Analysis (PCA) is merely considered in GT which is believed to be the proficient method to identify the part families. PCA classifies parts by allotting them to different families based on their resemblances in: (1) design characteristics such as shape and size, and/or (2) manufacturing characteristics (machining requirements). A novel approach based on simulated annealing namely SAPFOCS is adopted in this study to develop effective part families exploiting the PCA technique. Thereafter Taguchi's orthogonal design method is employed to solve the critical issues on the subject of parameters selection for the proposed metaheuristic algorithm. The adopted technique is therefore tested on 5 different datasets of size 5 {\times} 9 to 27 {\times} 9 and the obtained results are compared with C-Linkage clustering technique. The experimental results reported that the proposed metaheuristic algorithm is extremely effective in terms of the quality of the solution obtained and has outperformed C-Linkage algorithm in most instances.

Sustainable design is as an important movement in design. Biologically inspired design is a major paradigm for sustainable design. In this paper, we analyze a corpus of biologically inspired design projects in terms of sustainability. We then describe a case study of analogical design of a fog harvesting net, and abstract from it the patterns of Hydrophobia and Hydrophilia. We indicate how these two function-mechanism design patterns occur in several design projects in our corpus. This analysis indicates how biologically inspired sustainable design can be analyzed in terms of cross-domain analogical transfer of design patterns.

Bankruptcy prediction is very important for all the organization since it affects the economy and rise many social problems with high costs. There are large number of techniques have been developed to predict the bankruptcy, which helps the decision makers such as investors and financial analysts. One of the bankruptcy prediction models is the hybrid model using Fuzzy C-means clustering and MARS, which uses static ratios taken from the bank financial statements for prediction, which has its own theoretical advantages. The performance of existing bankruptcy model can be improved by selecting the best features dynamically depend on the nature of the firm. This dynamic selection can be accomplished by Genetic Algorithm and it improves the performance of prediction model..

Natural Immune system plays a vital role in the survival of the all living being. It provides a mechanism to defend itself from external predates making it consistent systems, capable of adapting itself for survival incase of changes. The human immune system has motivated scientists and engineers for finding powerful information processing algorithms that has solved complex engineering tasks. This paper explores one of the various possibilities for solving problem in a Multiagent scenario wherein multiple robots are deployed to achieve a goal collectively. The final goal is dependent on the performance of individual robot and its survival without having to lose its energy beyond a predetermined threshold value by deploying an evolutionary computational technique otherwise called the artificial immune system that imitates the biological immune system.

MAX-SAT heuristics normally operate from random initial truth assignments to the variables. We consider the use of what we call preambles, which are sequences of variables with corresponding single-variable assignment actions intended to be used to determine a more suitable initial truth assignment for a given problem instance and a given heuristic. For a number of well established MAX-SAT heuristics and benchmark instances, we demonstrate that preambles can be evolved by a genetic algorithm such that the heuristics are outperformed in a significant fraction of the cases.

The interaction networks of biological systems are known to take on several non-random structural properties, some of which are believed to positively influence system robustness. Researchers are only starting to understand how these structural properties emerge, however suggested roles for component fitness and community development (modularity) have attracted interest from the scientific community. In this study, we apply some of these concepts to an evolutionary algorithm and spontaneously organize its population using information that the population receives as it moves over a fitness landscape. More precisely, we employ fitness and clustering based driving forces for guiding network structural dynamics, which in turn are controlled by the population dynamics of an evolutionary algorithm. To evaluate the effect this has on evolution, experiments are conducted on six engineering design problems and six artificial test functions and compared against cellular genetic algorithms and 16 other evolutionary algorithm designs. Our results indicate that a self-organizing topology evolutionary algorithm exhibits surprisingly robust search behavior with promising performance observed over short and long time scales. After a careful analysis of these results, we conclude that the coevolution between a population and its topology represents a powerful new paradigm for designing robust search heuristics.

Engineered systems are designed to deftly operate under predetermined conditions yet are notoriously fragile when unexpected perturbations arise. In contrast, biological systems operate in a highly flexible manner; learn quickly adequate responses to novel conditions, and evolve new routines/traits to remain competitive under persistent environmental change. A recent theory on the origins of biological flexibility has proposed that degeneracy - the existence of multi-functional components with partially overlapping functions - is a primary determinant of the robustness and adaptability found in evolved systems. While degeneracy's contribution to biological flexibility is well documented, there has been little investigation of degeneracy design principles for achieving flexibility in systems engineering. Actually, the conditions that can lead to degeneracy are routinely eliminated in engineering design. With the planning of transportation vehicle fleets taken as a case study, this paper reports evidence that degeneracy improves robustness and adaptability of a simulated fleet without incurring costs to efficiency. We find degeneracy dramatically increases robustness of a fleet to unpredicted changes in the environment while it also facilitates robustness to anticipated variations. When we allow a fleet's architecture to be adapted in response to environmental change, we find degeneracy can be selectively acquired, leading to faster rates of design adaptation and ultimately to better designs. Given the range of conditions where favorable short-term and long-term performance outcomes are observed, we propose that degeneracy design principles fundamentally alter the propensity for adaptation and may be useful within several engineering and planning contexts.

Software testing is an expensive process, which is vital in the industry. Construction of the test-data in software testing requires the major cost and to decide which method to use in order to generate the test data is important. This paper discusses the efficiency of search-based algorithms (preferably genetic algorithm) versus random testing, in soft- ware test-data generation. This study differs from all previous studies due to sample programs (SUTs) which are used. Since we want to in- crease the complexity of SUTs gradually, and the program generation is automatic as well, Grammatical Evolution is used to guide the program generation. SUTs are generated according to the grammar we provide, with different levels of complexity. SUTs will first undergo genetic al- gorithm and then random testing. Based on the test results, this paper recommends one method to use for automation of software testing.