optimize article

2024/evolution/article.md

@@ -1,19 +1,6 @@
-# Adapt, Move, or Die: How Evolutionary Algorithms Solve Problems
-
-Evolutionary algorithms are a type of computer algorithm inspired by the process of natural evolution. They are designed to solve problems by mimicking how living organisms evolve over generations. These algorithms work by creating a population of potential solutions, evaluating their quality, and refining them through the processes of **selection**, **crossover**, **mutation**, and **iteration**. Over time, this "survival of the fittest" approach leads to increasingly effective solutions. 
-
-Imagine you're trying to find the best cookie recipe. You start with a group of random recipes. Each recipe is like an individual in a population.
-
-**Selection**: You bake cookies using these recipes and taste them. You keep the ones that taste the best. 
-
-**Crossover**: To create new recipes, you mix and match parts of the best ones. For example, you might take the chocolate chips from one recipe and the dough base from another. This is like combining "genes" from parents to make a new "child."
-
-**Mutation**: Sometimes, you try something new, like adding a pinch of cinnamon or using brown sugar instead of white. This random tweak might make the recipe even better—or worse.
-
-**Iteration**: You repeat this process: baking, tasting, selecting the best, mixing recipes, and tweaking. Over time, the recipes improve, and you get closer to the perfect cookie.
-
-Evolutionary algorithms work the same way, but instead of cookies, they’re solving problems, and instead of recipes, they’re working with potential solutions. The computer "evolves" solutions by mimicking natural evolution!
+# Darwin theory in Computer Science
 
+Evolutionary algorithms are a type of computer algorithm inspired by the process of natural evolution. They are designed to solve problems by mimicking how living organisms evolve over generations. These algorithms work by creating a population of potential solutions, evaluating their quality, and refining them through the processes of **selection**, **crossover** and  **mutation**. Over time, this "survival of the fittest" approach leads to increasingly effective solutions. 
 
 ## Authors
-- Ivan Sulima
+- Ivan Sulima