Evolution Algorithms--inspired by nature but invented by humans, Alife evolution inventions Research Papers (original) (raw)

Direct Carbon Capture Machines are now being field tested in several countries. These machines promise to be part of the tool box — among many technologies — for dealing with CO2 mitigation. As machines directed to ecological goals, they... more

Direct Carbon Capture Machines are now being field tested in several countries. These machines promise to be part of the tool box — among many technologies — for dealing with CO2 mitigation. As machines directed to ecological goals, they arrive at a time to collaborate with organisms and programming underpinning artificial intelligence (AI) and living technology. Here, I specifically look to AI, ALife, and synthetic biology in relation to microbial and plant intelligences to ask whether next generations of direct-air capture machines might be theoretically hybridized with the intelligence of bio-performative microbes monitored by AI (on the order of Google’s DeepMind) to evolve thin, lightweight, metabolic panels for urban sculpture, buildings, and infrastructures.
I focus on theoretical systems to justify symbiotic, biochemical, and self-sustaining bacterial life (e.g. carbon eating microbes) living in association with AI hosts. If artificial intelligence connects with molecular bacterial languages (codes, pictures, symbols, logics) and, if they can be decrypted, then the realms of environmental (plant/microbe) signaling may be networked between nature, AI, buildings, and metabolic machines (e.g. CO2 capturing devices). This symbiotic speculation makes possible imaging non-neurological, living/machine performance between skyscrapers, urban infrastructures, and nature. My proposition then supports next-phase design necessitating pathways over which we may hybridize AI and biological intelligence in machines/buildings as agents in the discovery of theory and methods for metabolic architectures and cities.

Bin Packing Problem (BPP) is a Combinatorial Optimization problem, which is used to find the optimal object from a finite set of objects. The purpose of BPP is to pack the items with different weight into finite number of bins without... more

Bin Packing Problem (BPP) is a Combinatorial Optimization problem, which is used to find the optimal object from a finite set of objects. The purpose of BPP is to pack the items with different weight into finite number of bins without exceeding its capacity. The main objective of this problem is to minimize the number of bins used and pack the items efficiently. This paper reviews a general idea of BPP and various algorithms which are used to solve the BPP. In this work, two heuristic algorithms First-Fit algorithm and Best-Fit algorithm are implemented and tested with well-known benchmark instances. This paper also discusses the algorithms which are inspired by Biology, such as Ant Colony Optimization algorithm (ACO), Cuckoo search and Genetic algorithm and their applications to Combinatorial Optimization problems.

In this paper, we present a new alternative method for text steganalysis based on an evolution algorithm, implemented using the Java Evolution Algorithms Package (JEAP).The main objective of this paper is to detect the existence of hidden... more

In this paper, we present a new alternative method for text steganalysis based on an evolution algorithm, implemented using the Java Evolution Algorithms Package (JEAP).The main objective of this paper is to detect the existence of hidden messages ased on fitness values of a text description.It is found that the detection performance has been influenced by two groups of fitness values which are good fitness value and bad fitness value. This paper provides a valuable insight into the development and enhancement of the text steganalysis domain.

"The objective of this proposal is to implement a school day agenda focused on the learning rhythms of students of elementary and secondary schools using a genetic algorithm. The methodology of this proposal takes into account legal... more

"The objective of this proposal is to implement a school day agenda focused on the learning rhythms of students of elementary and secondary schools using a genetic algorithm. The methodology of this proposal takes into account legal requirements and constraints on the assignment of teachers and classrooms in public educational institutions in Colombia. In addition, this proposal provides a set of constraints focused on cognitive rhythms and subjects are scheduled at the most convenient times according to the area of knowledge. The genetic algorithm evolves through a process of mutation and selection and builds a total solution based on the best solutions for each group. Sixteen groups in a school are tested and the results of class schedule assignments are presented. The quality of the solution obtained through the established approach is validated by comparing the results to the solutions obtained using another algorithm."