<prompt> Likarolo tsa bohlokoa tsa algorithm ea liphatsa tsa lefutso ke life?nan  <response>: Instruction in English: What are the key components of a genetic algorithm? . Response in English: A genetic algorithm is a computational method based on the principles of natural evolution and genetics, used to optimize problems or generate new solutions. The key components of a genetic algorithm are:1. Population: A set of potential solutions characteristically represented in binary strings or chromosomes. Each solution within the population is called an individual or a chromosome.2. Fitness Function: A function that evaluates the fitness of each individual in the population. The fitness value represents how well an individual solves the problem at hand.3. Selection: A process by which individuals with higher fitness values are selected to proceed to the next generation. Selection methods can include roulette wheel selection, tournament selection or ranking selection.4. Crossover: Also called recombination, it is an operator that combines the genetic information of two parent individuals to produce new offspring. Crossover techniques includes one-point crossover, multi-point crossover or uniform crossover.5. Mutation: A random change in the genetic makeup of an individual to maintain diversity within the population. Mutation techniques include bit-flip mutation, swap mutation or inversion mutation.6. Replacement: After selection, crossover, and mutation create new individuals, the new population must be chosen from both the old and new individuals. A replacement strategy is used to determine which individuals will be retained.7. Termination: The genetic algorithm terminates when a stopping criterion is met. Termination criteria can include reaching a certain number of generations, finding an acceptable solution, or when there is a lack of significant improvement in the population. . Response in Sesotho: Algorithm ea liphatsa tsa lefutso ke mokhoa oa computational o thehiloeng ho melao-motheo ea ho iphetola ha lintho tsa tlhaho le liphatsa tsa lefutso, tse sebelisetsoang ho ntlafatsa mathata kapa ho hlahisa tharollo e ncha. Likarolo tsa bohlokoa tsa algorithm ea liphatsa tsa lefutso ke: 1. Population: Sete ea tharollo e ka bang teng e emeloa ka hara likhoele tsa binary kapa li-chromosome. Tharollo ka ngoe ka hara baahi e bitsoa motho kapa chromosome. 2. Mosebetsi oa Boikoetliso: Mosebetsi o hlahlobang ho lekana ha motho ka mong sechabeng. Boleng ba ho ikoetlisa bo bontša kamoo motho a rarollang bothata boo a leng ho bona hantle. 3. Khetho: Mokhoa oo ka oona batho ba nang le litekanyetso tse phahameng tsa ho ikoetlisa ba khethoang ho fetela molokong o latelang. Mekhoa ea khetho e ka kenyelletsa khetho ea lebili la roulette, khetho ea tlholisano kapa khetho ea maemo. 4. Crossover: E boetse e bitsoa recombination, ke mochine o kopanyang boitsebiso ba liphatsa tsa lefutso tsa batsoali ba babeli ho hlahisa bana ba bacha. Mekhoa ea crossover e kenyelletsa crossover ea ntlha e le ngoe, crossover ea lintlha tse ngata kapa crossover e tšoanang. 5. Phetoho ea liphatsa tsa lefutso: Phetoho e sa lebelloang ea liphatsa tsa lefutso tsa motho ho boloka mefuta-futa ka hara baahi. Mekhoa ea phetoho e kenyelletsa phetoho ea bit-flip, swap mutation kapa inversion mutation. 6. Ho nkela sebaka: Ka mora hore khetho, crossover, le phetoho ea liphatsa tsa lefutso li hlahise batho ba bacha, palo e ncha e tlameha ho khethoa ho batho ba khale le ba bacha. Ho sebelisoa leano la ho nkela sebaka ho fumana hore na ke batho bafe ba tla bolokoa. 7. Ho felisoa: Algorithm ea liphatsa tsa lefutso e fela ha ho finyelloa mokhoa oa ho emisa. Mekhoa ea ho felisa e ka kenyelletsa ho finyella palo e itseng ea meloko, ho fumana tharollo e amohelehang, kapa ha ho se na ntlafatso e kholo ea baahi. 