Optimization of parameters or "systems" in general plays an ever-increasing role in mathematics, economics, engineering, and the life sciences. As a result, a wide variety of both traditional mathematical and nontraditional algorithmic approaches have been introduced to solve challenging and practically relevant optimization problems. Evolutionary optimization methods~in the form of genetic algorithms, genetic programming, and evolution strategies~represent nontraditional optimization algorithms that draw inspiration from the processes of natural evolution. Particle swarm optimization is another set of more recently developed algorithmic optimizers inspired by social behaviors of organisms such as birds [1] and social insects. These new evolutionary approaches in optimization are now entering the stage and are becoming very successful tools for solving real-world optimization problems [2]. We present Visplore and Evolvica as a toolkit to investigate, explore, and visualize evolutionary and swarm-based optimization techniques. A webMathematica interface is also available. ‡ 1. IntroductionThe evolutionary optimization methods of the genetic algorithm (GA) [3], genetic programming (GP) [4], and evolution strategy (ES) [5] are a branch of nontraditional optimization methods drawing inspiration from the processes of natural evolution. The particle swarm optimizer (PSO), on the other hand, is inspired by the social behavior of bird flocking [6]. Recently, we have been investigating the performance of evolution-and swarm-based optimizers in the domain of biomechanics, which we developed with the Human Performance Laboratory at the Faculty of Kinesiology, University of Calgary [7|9]. In this particular biomechanical application, numerical optimization algorithms are used to design equipment for sports activities. The involved simulations of muscle movements are very time consuming and high dimensional, thus making their evaluation costly and difficult. Simulating a soccer kick is an example of a model that investigates muscle activation patterns within the leg and foot when kicking a soccer ball toward the goal. The specific objective in this case is to obtain a high ball speed, in order to minimize the goal keeper's chances of catching the ball. In 1998, Cole applied a (1+ l) ES to this model [7,8]. More recently, we presented improved adaptations of the model parameters through a PSO [2,10].